Nonlinear Function (nonlinear + function)

Distribution by Scientific Domains

Selected Abstracts

Streamlining ,search and destroy': cost-effective surveillance for invasive species management

Cindy E. Hauser
Abstract Invasive species surveillance has typically been targeted to where the species is most likely to occur. However, spatially varying environmental characteristics and land uses may affect more than just the probability of occurrence. Biodiversity or economic value, and the ease of detection and control are also likely to vary. We incorporate these factors into a detection and treatment model of a low-density invader to determine the surveillance strategy that minimizes expected management costs. Sites with a high probability of invader occurrence and great benefits associated with detection warrant intensive surveillance; however, the optimum investment is a nonlinear function of these factors. Environments where the invader is relatively easy to detect are prioritized for surveillance, although only a moderate investment is necessary to ensure a high probability of detection. Intensive surveillance effort may be allocated to other sites if the probability of occurrence, budget and/or expected benefits is sufficiently high. [source]

Nonlinear Smoluchowski velocity for electroosmosis of Power-law fluids over a surface with arbitrary zeta potentials

Cunlu Zhao
Abstract Electroosmotic flow of Power-law fluids over a surface with arbitrary zeta potentials is analyzed. The governing equations including the nonlinear Poisson,Boltzmann equation, the Cauchy momentum equation and the continuity equation are solved to seek exact solutions for the electroosmotic velocity, shear stress, and dynamic viscosity distributions inside the electric double layer. Specifically, an expression for the general Smoluchowski velocity is obtained for electroosmosis of Power-law fluids in a fashion similar to the classic Smoluchowski velocity for Newtonian fluids. The existing Smoluchowski slip velocities under two special cases, (i) for Newtonian fluids with arbitrary zeta potentials and (ii) for Power-law fluids with small zeta potentials, can be recovered from our derived formula. It is interesting to note that the general Smoluchowski velocity for non-Newtonian Power-law fluids is a nonlinear function of the electric field strength and surface zeta potentials; this is due to the coupling electrostatics and non-Newtonian fluid behavior, which is different from its counterpart for Newtonian fluids. This general Smoluchowski velocity is of practical significance in determining the flow rates in microfluidic devices involving non-Newtonian Power-law fluids. [source]

Enhanced dechlorination of trichloroethylene by membrane-supported Pd-coated iron nanoparticles

Linfeng Wu
Abstract In this study, cellulose acetate (CA) supported iron and Pd/Fe nanoparticles were used for dechlorination of trichloroethylene (TCE) from water. Solution and microemulsion methods were used to synthesize the iron nanoparticles. Pd/Fe bimetallic particles were prepared by postcoating Pd on the prepared iron nanoparticles. These materials were then dispersed in CA solution, which was used to prepare the membrane-supported nanoparticles. TEM imaging confirmed that the iron and Pd/Fe bimetallic nanoparticles were ,10 nm in diameter. The results of dechlorination studies showed that the surface composition of the Pd/Fe bimetallic nanoparticles (microemulsion method) significantly affected the observed reduction rate constant. In addition, the rate constant was a nonlinear function of metal loading and initial TCE concentration. A comparative study for the Pd/Fe (Pd 1.9 wt %) nanoparticles from solution and microemulsion methods showed that the nanoparticles formed by the latter method gave superior performance for the dechlorination of TCE. © 2008 American Institute of Chemical Engineers Environ Prog, 2008 [source]

NMDA receptor-mediated metaplasticity during the induction of long-term depression by low-frequency stimulation

Bruce Mockett
Abstract Metaplasticity refers to the activity-dependent modification of the ability of synapses to undergo subsequent synaptic plasticity. Here, we have addressed the question of whether metaplasticity contributes to the induction of long-term depression (LTD) by low-frequency stimulation (LFS). The experiments were conducted using standard extracellular recording techniques in stratum radiatum of area CA1 in hippocampal slices made from adult Sprague,Dawley rats. The degree of LTD induction was found to be a nonlinear function of the number of pulses during a 1-Hz LFS. Little LTD was observed following 600 or 900 pulses, but a significant LTD occurred following 1200 pulses of LFS, whether delivered in one episode, or in two bouts of 600 pulses given 10 min apart. A similar pattern was observed for 3 Hz LFS. The data support the suggestion that pulses occurring early in the LFS train prime synapses for LTD induction, as triggered by later occurring stimuli. The priming effect lasted at least 120 min, when tested by giving two bouts of 1 Hz LFS (600 pulses each) at different intervals. Neither heterosynaptic nor homosynaptic stimulation by itself was sufficient to prime LTD. However, a combination of the stimuli, induced by increased stimulus strength during the LFS, appeared necessary for inducing the effect. An N -methyl- d -aspartate (NMDA) receptor antagonist markedly reduced total LTD induction, regardless of whether it was administered during the first or second LFS in a protocol employing two bouts of 600 pulse LFS, 30 min apart. These findings strongly support the hypothesis that NMDA receptor-dependent metaplasticity processes contribute to the induction of LTD during standard LFS protocols. [source]

Source density-driven independent component analysis approach for fMRI data

Baoming Hong
Abstract Independent component analysis (ICA) has become a popular tool for functional magnetic resonance imaging (fMRI) data analysis. Conventional ICA algorithms including Infomax and FAST-ICA algorithms employ the underlying assumption that data can be decomposed into statistically independent sources and implicitly model the probability density functions of the underlying sources as highly kurtotic or symmetric. When source data violate these assumptions (e.g., are asymmetric), however, conventional ICA methods might not work well. As a result, modeling of the underlying sources becomes an important issue for ICA applications. We propose a source density-driven ICA (SD-ICA) method. The SD-ICA algorithm involves a two-step procedure. It uses a conventional ICA algorithm to obtain initial independent source estimates for the first-step and then, using a kernel estimator technique, the source density is calculated. A refitted nonlinear function is used for each source at the second step. We show that the proposed SD-ICA algorithm provides flexible source adaptivity and improves ICA performance. On SD-ICA application to fMRI signals, the physiologic meaningful components (e.g., activated regions) of fMRI signals are governed typically by a small percentage of the whole-brain map on a task-related activation. Extra prior information (using a skewed-weighted distribution transformation) is thus additionally applied to the algorithm for the regions of interest of data (e.g., visual activated regions) to emphasize the importance of the tail part of the distribution. Our experimental results show that the source density-driven ICA method can improve performance further by incorporating some a priori information into ICA analysis of fMRI signals. Hum Brain Mapping, 2005. © 2005 Wiley-Liss, Inc. [source]

Identification of continuous-time nonlinear systems by using a gaussian process model

Tomohiro Hachino Member
Abstract This paper deals with a nonparametric identification of continuous-time nonlinear systems by using a Gaussian process model. Genetic algorithm is applied to train the Gaussian process prior model by minimizing the negative log marginal likelihood of the identification data. The nonlinear term of the objective system is estimated as the predictive mean function of the Gaussian process, and the confidence measure of the estimated nonlinear function is given by the predictive covariance function of the Gaussian process. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source]

Neural network-based adaptive attitude tracking control for flexible spacecraft with unknown high-frequency gain

Qinglei Hu
Abstract Adaptive control design using neural networks (a) is investigated for attitude tracking and vibration stabilization of a flexible spacecraft, which is operated at highly nonlinear dynamic regimes. The spacecraft considered consists of a rigid body and two flexible appendages, and it is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension as well, for the purpose of design. Based on this nonlinear model, the derivation of an adaptive control law using neural networks (NNs) is treated, when the dynamics of unstructured and state-dependent nonlinear function are completely unknown. A radial basis function network that is used here for synthesizing the controller and adaptive mechanisms is derived for adjusting the parameters of the network and estimating the unknown parameters. In this derivation, the Nussbaum gain technique is also employed to relax the sign assumption for the high-frequency gain for the neural adaptive control. Moreover, systematic design procedure is developed for the synthesis of adaptive NN tracking control with L2 -gain performance. The resulting closed-loop system is proven to be globally stable by Lyapunov's theory and the effect of the external disturbances and elastic vibrations on the tracking error can be attenuated to the prescribed level by appropriately choosing the design parameters. Numerical simulations are performed to show that attitude tracking control and vibration suppression are accomplished in spite of the presence of disturbance torque/parameter uncertainty. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Neural network-based adaptive control of piezoelectric actuators with unknown hysteresis

Wen-Fang Xie
Abstract This paper proposes a neural network (NN)-based adaptive control of piezoelectric actuators with unknown hysteresis. Based on the classical Duhem model described by a differential equation, the explicit solution to the equation is explored and a new hysteresis model is constructed as a linear model in series with a piecewise continuous nonlinear function. An NN-based dynamic pre-inversion compensator is designed to cancel out the effect of the hysteresis. With the incorporation of the pre-inversion compensator, an adaptive control scheme is proposed to have the position of the piezoelectric actuator track the desired trajectory. This paper has three distinct features. First, it applies the NN to online approximate complicated piecewise continuous unknown nonlinear functions in the explicit solution to Duhem model. Second, an observer is designed to estimate the output of hysteresis of piezoelectric actuator based on the system input and output. Third, the stability of the controlled piezoelectric actuator with the observer is guaranteed. Simulation results for a practical system validate the effectiveness of the proposed method in this paper. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Modeling power and intermodulation behavior of microwave transistors with unified small-signal/large-signal neural network models

F. Giannini
Abstract This article presents a detailed procedure to learn a nonlinear model and its derivatives to as many orders as desired with multilayer perceptron (MLP) neural networks. A modular neural network modeling a nonlinear function and its derivatives is introduced. The method has been used for the extraction of the large-signal model of a power MESFET device, modeling the nonlinear relationship of drain-source current Ids as well as gate and drain charge Qg and Qd with respect to intrinsic voltages Vgs and Vds over the whole operational bias region. The neural models have been implemented into a user-defined nonlinear model of a commercial microwave simulator to predict output power performance as well as intermodulation distortion. The accuracy of the device model is verified by harmonic load-pull measurements. This neural network approach has demonstrated to predict nonlinear behavior with enough accuracy even if based only on first-order derivative information. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 276,284, 2003. [source]

Distribution of infestation by lentil gall midge Contarinia lentis (Dipt., Cecidomyiidae) in lentil fields: statistical model

P. Kolesik
The horizontal distribution of infestation by Contarinia lentis in lentil fields, not subjected to chemical control was recorded and analysed in Slovak Republic during outbreaks of this pest in 1986,89. Infestation level was defined as the percentage of galls form the total number of galls, pods, and flowers. The highest level of infestation were found along the edges and the lowest levels in the centres of fields. The distribution of the infestation between the edge and the centre has been described for 18 fields using a nonlinear function containing two parameters. The first parameter represents the infestation level at the edge. The second parameter represents the rate of decreas of infestation from the edge towards the centre of the field. The relationship between the first parameter and the distance to the adult midge emerge site and the relationship between second parameter and the mean velocity of winds caryring females from the emergence site is described using exponential functions. The results indicate that (1) the longer the distance to the midge emergence site, the lower the infestation at the edge; and (2) the gretaer the wind velocity, the weaker the decrese in infestation from the edge towards the centre of a field. [source]


ABSTRACT The goal in a multi-objective function optimization problem is to optimize the several objective functions simultaneously. the complex method is a powerful algorithm to find the optimum of a general nonlinear function within a constrained region. the objective of this study was to apply the complex method to two different shapes (a sphere and a finite cylinder) subjected to the same thermal processing boundary conditions to find a variable process temperature profile (decision variable) to maximize the volume-average retention of thiamine. A process temperature range of 5 to 150C was used as an explicit constraint. Implicit constraints were center temperature and accumulated center lethality of the sphere and the finite cylinder. the objective functions for both shapes were combined into a single one using a weighting method. Then, the previously developed complex algorithm was applied using Lexicographic Ordering to order the objective functions with respect to their significance. the results were reported as optimum variable process temperature profiles using the given geometries and objective functions. the thiamine retentions were also compared with a constant process temperature process, and 3.0% increase was obtained in the combined objective function. the results showed that the complex method can be successfully used to predict the optimum variable process temperature profiles in multi-criteria thermal processing problems. [source]

Direct analysis of 15N-label in amino and amide groups of glutamine and asparagine

Anne Marie Scharff-Poulsen
Abstract A novel method for on-line determination of the amount and position of 15N-labeling in complex mixtures of amino acids is presented. Underivatized amino acids were analyzed by ion-pair chromatography in combination with mass spectrometry. This enables the direct determination of the 15N label distribution. The fragmentation pathways of the nitrogen moieties of glutamine (Gln) and asparagine (Asn) were studied in detail using all mono 15N isotopomers, which led to a method for differentiating between 15N-amide and 15N-amino labeling. The fragmentation involving the amino and amide groups of Gln led to distinct ion structures. The equivalent fragmentation pattern was not observed for Asn. Instead, the amide group of Asn was eliminated as HNCO in a secondary process. The developed analytical method was evaluated by analysis of a range of standard mixtures taking into account different levels of 15N abundance and distribution between the amino and amide groups. The detection limit (3 SD) for the presence of a 15N label was 0.7 and 1.0% for Gln and Asn, respectively. The determination of the positional labeling follows a nonlinear function. A representative example at 30% 15N was used as a benchmark resulting in average relative standard deviations of 2.7 and 15% for Gln and Asn, respectively. The corresponding expectation windows for the positional labeling were found to be 2 and 12%, respectively. Copyright © 2006 John Wiley & Sons, Ltd. [source]


Dry soybean seeds were soaked in water for 15 to 300 min at temperatures of 50C, 60C, 70C, 80C and then compressed 75% two times with an Instron in a texture profile analysis type of test. A multiple regression analysis showed a nonlinear function of time and temperature of hydration for all the three textural parameters and can be used for determining the blanching conditions for obtaining any desirable texture of the product. [source]

Application in stochastic volatility models of nonlinear regression with stochastic design

Ping Chen
Abstract In regression model with stochastic design, the observations have been primarily treated as a simple random sample from a bivariate distribution. It is of enormous practical significance to generalize the situation to stochastic processes. In this paper, estimation and hypothesis testing problems in stochastic volatility model are considered, when the volatility depends on a nonlinear function of the state variable of other stochastic process, but the correlation coefficient |,|,±1. The methods are applied to estimate the volatility of stock returns from Shanghai stock exchange. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Structural design of composite nonlinear feedback control for linear systems with actuator constraint,

Weiyao Lan
Abstract The performance of the composite nonlinear feedback (CNF) control law relies on the selection of the linear feedback gain and the nonlinear function. However, it is a tough task to select an appropriate linear feedback gain and appropriate parameters of the nonlinear function because the general design procedure of CNF control just gives some simple guidelines for the selections. This paper proposes an operational design procedure based on the structural decomposition of the linear systems with input saturation. The linear feedback gain is constructed by two linear gains which are designed independently to stabilize the unstable zero dynamics part and the pure integration part of the system respectively. By investigating the influence of these two linear gains on transient performance, it is flexible and efficient to design a satisfactory linear feedback gain for the CNF control law. Moreover, the parameters of the nonlinear function are tuned automatically by solving a minimization problem. The proposed design procedure is illustrated by applying it to design a tracking control law for the inverted pendulum on a cart system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Observers for a class of nonlinear systems with time-delay,

Lei Zhou
Abstract In this paper, the issue of observer designs for a class of nonlinear continuous-time systems with time-delay is addressed, where the nonlinear function is not necessarily Lipschitz. It is shown that both full-order and reduced-order observers can be obtained by means of the same linear matrix inequality. A numerical example is presented to show the effectiveness of the proposed approach. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Nonlinear Control VIA Generalized Feedback Linearization Using Neural Networks

Graham C. Goodwin
ABSTRACT A novel approach to nonlinear control, called Generalized Feedback Linearization (GFL), is presented. This new strategy overcomes one important drawback of the well known Feedback Linearization strategy, in the sense that it is able to handle a broader class of nonlinear systems, namely those having unstable zero dynamics. It is shown that the use of a nonlinear predictor for the system output is a key feature in the derivation of the control strategy. For certain types of systems this predictor can be found as a nonlinear function of the system input and output, allowing an output feedback control solution. The use of Artificial Neural Networks (ANN) to directly parameterize the predictor of the controlled variable when an explicit model for the system is not available, is investigated via computer simulations. This approach is based on the functional approximation capability of multi layer ANN. [source]

Unanticipated impacts of spatial variance of biodiversity on plant productivity

Lisandro Benedetti-Cecchi
Abstract Experiments on biodiversity have shown that productivity is often a decelerating monotonic function of biodiversity. A property of nonlinear functions, known as Jensen's inequality, predicts negative effects of the variance of predictor variables on the mean of response variables. One implication of this relationship is that an increase in spatial variability of biodiversity can cause dramatic decreases in the mean productivity of the system. Here I quantify these effects by conducting a meta-analysis of experimental data on biodiversity,productivity relationships in grasslands and using the empirically derived estimates of parameters to simulate various scenarios of levels of spatial variance and mean values of biodiversity. Jensen's inequality was estimated independently using Monte Carlo simulations and quadratic approximations. The median values of Jensen's inequality estimated with the first method ranged from 3.2 to 26.7%, whilst values obtained with the second method ranged from 5.0 to 45.0%. Meta-analyses conducted separately for each combination of simulated values of mean and spatial variance of biodiversity indicated that effect sizes were significantly larger than zero in all cases. Because patterns of biodiversity are becoming increasingly variable under intense anthropogenic pressure, the impact of loss of biodiversity on productivity may be larger than current estimates indicate. [source]

Neural network-based adaptive control of piezoelectric actuators with unknown hysteresis

Wen-Fang Xie
Abstract This paper proposes a neural network (NN)-based adaptive control of piezoelectric actuators with unknown hysteresis. Based on the classical Duhem model described by a differential equation, the explicit solution to the equation is explored and a new hysteresis model is constructed as a linear model in series with a piecewise continuous nonlinear function. An NN-based dynamic pre-inversion compensator is designed to cancel out the effect of the hysteresis. With the incorporation of the pre-inversion compensator, an adaptive control scheme is proposed to have the position of the piezoelectric actuator track the desired trajectory. This paper has three distinct features. First, it applies the NN to online approximate complicated piecewise continuous unknown nonlinear functions in the explicit solution to Duhem model. Second, an observer is designed to estimate the output of hysteresis of piezoelectric actuator based on the system input and output. Third, the stability of the controlled piezoelectric actuator with the observer is guaranteed. Simulation results for a practical system validate the effectiveness of the proposed method in this paper. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Adaptive backstepping control for a class of time delay systems with nonlinear perturbations

Chang-Chun Hua
Abstract The sliding mode control method has been extensively employed to stabilize time delay systems with nonlinear perturbations. Although the resulting closed-loop systems have good transient and steady-state performances, the designed controllers are dependent on the time delays. But one knows that it is difficult to obtain the precise delay time in practical systems, especially when it is time varying. In this paper, we revisit the problem and use the backstepping method to construct the state feedback controller. First, a coordinate transformation is used to obtain a cascade time delay system. Then, a linear virtual control law is designed for the first subsystem. The memoryless controller is further constructed based on adaptive method for the second subsystem with the uncertainties bounded by linear function. By choosing new Lyapunov,Krasovskii functional, we show that the system state converges to zero asymptotically. Via the proposed approach, we also discuss the case that the uncertainties are bounded by nonlinear functions. Finally, simulations are done to verify the effectiveness of the main results obtained. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Linear PI control of batch exothermic reactors with temperature measurement

Jose Alvarez-Ramirez
Abstract A wide variety of speciality materials and fine chemicals such as plastics, pharmaceutical and microelectronics components are produced in batch reactors. The nonlinear, transient and finite-time features of the batch reactors give rise to complex process and control design problems. In particular, the safe operation of exothermic reactors depends on the adequate functioning of a temperature tracking controller, and to a good extent, the same is true for the attainment of a suitable compromise between productivity and product quality attributes. While the stabilization problem of continuous exothermic chemical reactors has been recently addressed with rigorous asymptotic-stability methods, the same kind of studies have not yet been performed for the finite-time batch reactor case. In this paper, the problem of designing a temperature tracking controller for an exothermic batch reactor, with n species and m reactions, is addressed under the following premises: (i) only the reactor temperature is measured, (ii) the (typically uncertain) reaction rate and heat exchange nonlinear functions are unknown, (iii) the controller must be linear and easy to tune, and (iv) the closed-loop reactor motion must be stable in a suitable sense. The combination of industrial-oriented inventory control concepts in conjunction with singular perturbation results yields a linear controller with a combined feedforward-PI feedback structure, antireset windup scheme, and conventional-like tuning rules. The controller: (i) tracks, arbitrarily fast and close, a prescribed temperature trajectory, with admissibly deviated concentration motions, and (ii) quickly recovers the behaviour of an exact model-based nonlinear I/O linearizing controller. The proposed design is put in perspective with the geometric and IMC nonlinear control approaches. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Adaptive backstepping control for a class of nonlinear systems using neural network approximations

K. K. Tan
In this paper, an adaptive neural network (NN) backstepping technique is developed for tracking control of a class of nonlinear systems. NNs are used to compensate for the unknown nonlinear functions in the system. A systematic backstepping approach is established to synthesize the adaptive NN control scheme that ensures the boundedness of all the signals in the closed-loop system, and yields a small tracking error. The issue of transient performance is also addressed under an analytical framework. The effectiveness of the proposed scheme is demonstrated by computer simulations. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Efficient MILP formulations for the simultaneous optimal peptide tag design and downstream processing synthesis

AICHE JOURNAL, Issue 9 2009
João M. Natali
Abstract Novel and efficient linear formulations are developed for the problem of simultaneously performing an optimal synthesis of chromatographic protein purification processes, and the concomitant selection of peptide purification tags, that result in a maximal process improvement. To this end, two formulations are developed for the solution of this problem: (1) a model that minimizes both the number of chromatographic steps in the final purification process flow sheet and the composition of the tag, by use of weighted objectives, while satisfying minimal purity requirements for the final product; and (2) a model that attempts to find the maximal attainable purity under constraints on the maximum number of separation techniques and tag size. Both models are linearized using a previously developed strategy for obtaining optimal piecewise linear approximations of nonlinear functions. Proposed are models to two case studies based on protein mixtures with different numbers of proteins. Results show that the models are capable of solving to optimality all the implemented cases with computational time requirements of under 1 s, on average. The results obtained are further compared with previous nonlinear and linear models attempting to solve the same problem, and, thus, show that the approach represents significant gains in robustness and efficiency. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source]

Step and pulse response methods for identification of wiener processes

AICHE JOURNAL, Issue 2 2006
Ho Cheol Park
Abstract Lack of simple identification methods for nonlinear processes hinders field applications of nonlinear control systems. For identification methods that are as simple as those for the first order plus time delay models of linear dynamical processes, graphical and least squares methods to identify Wiener-type nonlinear processes from standard responses, such as step, pulse, and square-wave responses, are proposed. Static nonlinear functions are identified independently in Wiener-type nonlinear processes. Graphical methods extract discrete points of the nonlinear static function or a continuous non-parametric model of the nonlinear static function iteratively. The least squares method provides a parametric model of the nonlinear static function. The identified static nonlinear function can be used to design a simple linearizing control system. To illustrate the proposed identification methods, simulation and experimental results are given. © 2005 American Institute of Chemical Engineers AIChE J, 2006 [source]

On the properties of the periodogram of a stationary long-memory process over different epochs with applications

Valdério A. Reisen
Primary 60G10; 60K35; Secondary 60G18 This article studies the asymptotic properties of the discrete Fourier transforms (DFT) and the periodogram of a stationary long-memory time series over different epochs. The main theoretical result is a novel bound for the covariance of the DFT ordinates evaluated on two distinct epochs, which depends explicitly on the Fourier frequencies and the gap between the epochs. This result is then applied to obtain the limiting distribution of some nonlinear functions of the periodogram over different epochs, under the additional assumption of gaussianity. We then apply this result to construct an estimator of the memory parameter based on the regression in a neighbourhood of the zero-frequency of the logarithm of the averaged periodogram, obtained by computing the empirical mean of the periodogram over adjacent epochs. It is shown that replacing the periodogram by its average has an effect similar to the frequency domain pooling to reduce the variance of the estimate. We also propose a simple procedure to test the stationarity of the memory coefficient. A limited Monte Carlo experiment is presented to support our findings. [source]

Characterization of the Vestibulo-Ocular Reflex Evoked by High-Velocity Movements

François D. Roy HBSc
Abstract Objectives/Hypothesis: The horizontal angular vestibulo-ocular reflex (VOR) plays an important role in stabilizing images on the retina throughout head rotations. Current evidence suggests that the VOR behaves linearly at low velocities and nonlinearly at high velocities. The aim of the research was to evaluate and characterize the normal behavior of the reflex evoked by high-velocity head rotations. Study Design: Case control study. Methods: Manually applied head-thrust movements with peak velocities in the range of 100° to 500°/s and peak accelerations up to 7,000°/s 2 were performed on normal volunteers. These head thrusts were comparable with those described in detail by Halmagi and coworkers. Eye and head movements were recorded using the magnetic search coil method. Results: The gain of the VOR is linear at low velocities and saturates at head velocities greater than 350°/s. The values for the normal gain of the reflex were approximated by means of the area between two nonlinear functions. The directional difference parameter, exploring the symmetry of the reflex, indicated that the VOR in normal subjects is symmetric. Conclusion: The gain of the VOR in individuals with intact vestibular function is nonlinear at high angular head velocities. We propose a quantitative means using two nonlinear functions to characterize the normal range of values for the gain of the VOR in individuals with normal vestibular function. A directional difference parameter used in conjunction with the normal range of gains can detect small differences in the symmetry of the VOR and, consequently, reveal unilateral vestibular loss. [source]

Neural Network Adaptive Robust Control Of Siso Nonlinear Systems In A Normal Form

J.Q. Gong
ABSTRACT In this paper, performance oriented control laws are synthesized for a class of single-input-single-output (SISO) n -th order nonlinear systems in a normal form by integrating the neural networks (NNs) techniques and the adaptive robust control (ARC) design philosophy. All unknown but repeat-able nonlinear functions in the system are approximated by the outputs of NNs to achieve a better model compensation for an improved performance. While all NN weights are tuned on-line, discontinuous projections with fictitious bounds are used in the tuning law to achieve a controlled learning. Robust control terms are then constructed to attenuate model uncertainties for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy. Furthermore, if the unknown nonlinear functions are in the functional ranges of the NNs and the ideal NN weights fall within the fictitious bounds, asymptotic output tracking is achieved to retain the perfect learning capability of NNs. The precision motion control of a linear motor drive system is used as a case study to illustrate the proposed NNARC strategy. [source]

Bayesian Modeling of Differential Gene Expression

BIOMETRICS, Issue 1 2006
Alex Lewin
Summary We present a Bayesian hierarchical model for detecting differentially expressing genes that includes simultaneous estimation of array effects, and show how to use the output for choosing lists of genes for further investigation. We give empirical evidence that expression-level dependent array effects are needed, and explore different nonlinear functions as part of our model-based approach to normalization. The model includes gene-specific variances but imposes some necessary shrinkage through a hierarchical structure. Model criticism via posterior predictive checks is discussed. Modeling the array effects (normalization) simultaneously with differential expression gives fewer false positive results. To choose a list of genes, we propose to combine various criteria (for instance, fold change and overall expression) into a single indicator variable for each gene. The posterior distribution of these variables is used to pick the list of genes, thereby taking into account uncertainty in parameter estimates. In an application to mouse knockout data, Gene Ontology annotations over- and underrepresented among the genes on the chosen list are consistent with biological expectations. [source]