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Inference System (inference + system)
Kinds of Inference System Selected AbstractsShort-term MPEG-4 video traffic prediction using ANFISINTERNATIONAL JOURNAL OF NETWORK MANAGEMENT, Issue 6 2005Adel Abdennour Multimedia traffic and particularly MPEG-coded video streams are growing to be a major traffic component in high-speed networks. Accurate prediction of such traffic enhances the reliable operation and the quality of service of these networks through a more effective bandwidth allocation and better control strategies. However, MPEG video traffic is characterized by a periodic correlation structure, a highly complex bit rate distribution and very noisy streams. Therefore, it is considered an intractable problem. This paper presents a neuro-fuzzy short-term predictor for MPEG-4-coded videos. The predictor is based on the Adaptive Network Fuzzy Inference System (ANFIS) to perform single-step predictions for the I, P and B frames. Short-term predictions are also examined using smoothed signals of the video sequences. The ANFIS prediction results are evaluated using long entertainment and broadcast video sequences and compared to those obtained using a linear predictor. ANFIS is capable of providing accurate prediction and has the added advantage of being simple to design and to implement. Copyright © 2005 John Wiley & Sons, Ltd. [source] Fuzzy Sarsa Learning and the proof of existence of its stationary points,ASIAN JOURNAL OF CONTROL, Issue 5 2008Vali Derhami Abstract This paper provides a new Fuzzy Reinforcement Learning (FRL) algorithm based on critic-only architecture. The proposed algorithm, called Fuzzy Sarsa Learning (FSL), tunes the parameters of conclusion parts of the Fuzzy Inference System (FIS) online. Our FSL is based on Sarsa, which approximates the Action Value Function (AVF) and is an on-policy method. In each rule, actions are selected according to the proposed modified Softmax action selection so that the final inferred action selection probability in FSL is equivalent to the standard Softmax formula. We prove the existence of fixed points for the proposed Approximate Action Value Iteration (AAVI). Then, we show that FSL satisfies the necessary conditions that guarantee the existence of stationary points for it, which coincide with the fixed points of the AAVI. We prove that the weight vector of FSL with stationary action selection policy converges to a unique value. We also compare by simulation the performance of FSL and Fuzzy Q-Learning (FQL) in terms of learning speed, and action quality. Moreover, we show by another example the convergence of FSL and the divergence of FQL when both algorithms use a stationary policy. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] PREDICTING THE IMPACT OF ANTICIPATORY ACTION ON U.S. STOCK MARKET,AN EVENT STUDY USING ANFIS (A NEURAL FUZZY MODEL)COMPUTATIONAL INTELLIGENCE, Issue 2 2007P. Cheng In this study, the adaptive neural fuzzy inference system (ANFIS), a hybrid fuzzy neural network, is adopted to predict the actions of the investors (when and whether they buy or sell) in a stock market in anticipation of an event,changes in interest rate, announcement of its earnings by a major corporation in the industry, or the outcome of a political election for example. Generally, the model is relatively more successful in predicting when the investors take actions than what actions they take and the extent of their activities. The findings do demonstrate the learning and predicting potential of the ANFIS model in financial applications, but at the same time, suggest that some of the market behaviors are too complex to be predictable. [source] Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgetsEARTH SURFACE PROCESSES AND LANDFORMS, Issue 2 2010Joseph M. Wheaton Abstract Repeat topographic surveys are increasingly becoming more affordable, and possible at higher spatial resolutions and over greater spatial extents. Digital elevation models (DEMs) built from such surveys can be used to produce DEM of Difference (DoD) maps and estimate the net change in storage terms for morphological sediment budgets. While these products are extremely useful for monitoring and geomorphic interpretation, data and model uncertainties render them prone to misinterpretation. Two new methods are presented, which allow for more robust and spatially variable estimation of DEM uncertainties and propagate these forward to evaluate the consequences for estimates of geomorphic change. The first relies on a fuzzy inference system to estimate the spatial variability of elevation uncertainty in individual DEMs while the second approach modifies this estimate on the basis of the spatial coherence of erosion and deposition units. Both techniques allow for probabilistic representation of uncertainty on a cell-by-cell basis and thresholding of the sediment budget at a user-specified confidence interval. The application of these new techniques is illustrated with 5 years of high resolution survey data from a 1,km long braided reach of the River Feshie in the Highlands of Scotland. The reach was found to be consistently degradational, with between 570 and 1970,m3 of net erosion per annum, despite the fact that spatially, deposition covered more surface area than erosion. In the two wetter periods with extensive braid-plain inundation, the uncertainty analysis thresholded at a 95% confidence interval resulted in a larger percentage (57% for 2004,2005 and 59% for 2006,2007) of volumetric change being excluded from the budget than the drier years (24% for 2003,2004 and 31% for 2005,2006). For these data, the new uncertainty analysis is generally more conservative volumetrically than a standard spatially-uniform minimum level of detection analysis, but also produces more plausible and physically meaningful results. The tools are packaged in a wizard-driven Matlab software application available for download with this paper, and can be calibrated and extended for application to any topographic point cloud (x,y,z). Copyright © 2009 John Wiley & Sons, Ltd. [source] Using soil knowledge for the evaluation of mid-infrared diffuse reflectance spectroscopy for predicting soil physical and mechanical propertiesEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 5 2008B. Minasny Summary Mid-infrared diffuse reflectance spectroscopy can provide rapid, cheap and relatively accurate predictions for a number of soil properties. Most studies have found that it is possible to estimate chemical properties that are related to surface and solid material composition. This paper focuses on prediction of physical and mechanical properties, with emphasis on the elucidation of possible mechanisms of prediction. Soil physical properties that are based on pore-space relationships such as bulk density, water retention and hydraulic conductivity cannot be predicted well using MIR spectroscopy. Hydraulic conductivity was measured using a tension-disc permeameter, excluding the macropore effect, but MIR spectroscopy did not give a good prediction. Properties based on the soil solid composition and surfaces such as clay content and shrink-swell potential can be predicted reasonably well. Macro-aggregate stability in water can be predicted reasonably as it has a strong correlation with carbon content in the soil. We found that most of the physical and mechanical properties can be related back to the fundamental soil properties such as clay content, carbon content, cation exchange capacity and bulk density. These connections have been explored previously in pedotransfer functions studies. The concept of a spectral soil inference system is reiterated: linking the spectra to basic soil properties and connecting basic soil properties to other functional soil properties via pedotransfer functions. [source] Automatic diagnosis of diabetes using adaptive neuro-fuzzy inference systemsEXPERT SYSTEMS, Issue 4 2010Elif Derya Übeyli Abstract: A new approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records. The ANFIS classifier is used to detect diabetes diseases when eight features defining diabetes indications are used as inputs. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. The conclusions concerning the impacts of features on the diagnosis of diabetes disease are obtained through analysis of the ANFIS. The performance of the ANFIS model is evaluated in terms of training performances and classification accuracies and the results confirm that the proposed ANFIS model has potential in detecting diabetes diseases. [source] Application of an adaptive neuro-fuzzy inference system for classification of Behcet disease using the fast Fourier transform methodEXPERT SYSTEMS, Issue 2 2007Necaattin Bari Abstract: In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease. [source] Learning to recognize vulnerable patterns due to undesirable Zone-3 relay operationsIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 3 2009Koji Yamashita Member Abstract Undesirable zone 3 relay operations caused by unexpected loading conditions can contribute to the cascaded events, leading to catastrophic outages. Identifying the basic patterns of zone 3 relay operations in advance is an effective way to help prevent cascaded events. The postcontingency impedances seen by zone 3 relays can be calculated on line in a steady state security assessment framework. However, their accuracy is inadequate compared with the postcontingency apparent impedance obtained from off-line time domain dynamic simulations. This paper proposes a fuzzy inference system (FIS) to correct discrepancies between the postcontingency apparent impedances obtained from the results of steady state security assessment and the corresponding values obtained by time-domain simulations. The postcontingency apparent impedances obtained from the results of steady state security assessment can be corrected on line using correction terms provided by the FIS. The dynamic model of a 200-bus system is used to validate the performance of the proposed FIS. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Crack identification of a planar frame structure based on a synthetic artificial intelligence techniqueINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 1 2003Mun-Bo Shim Abstract It has been established that a crack has an important effect on the dynamic behaviour of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a planar frame structure, a method is presented in this paper which uses a synthetic artificial intelligence technique, i.e. adaptive-network-based fuzzy inference system (ANFIS) solved via a hybrid learning algorithm (the backpropagation gradient descent and the least-squares method) and continuous evolutionary algorithms (CEAs) solving single objective optimization problems with a continuous function and continuous search space efficiently are unified. With ANFIS and CEAs it is possible to formulate the inverse problem. ANFIS is used to obtain the input (the location and depth of a crack),output (the structural eigenfrequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this idea on 2D beam structures and the results are promising. Copyright © 2003 John Wiley & Sons, Ltd. [source] Interval type-2 fuzzy logic for edges detection in digital imagesINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2009Olivia Mendoza Edges detection in a digital image is the first step in an image recognition system. In this paper, we show an efficient edges detector using an interval type-2 fuzzy inference system (FIS-2). The FIS-2 uses as input the original images after applying Sobel filters and attenuation filters, then the fuzzy rules infer normalized values for the edges images, especially useful to enhance the performance of neural networks. To illustrate the results, we built frequency histograms of some images and compare the results of the FIS-2 edge's detector with the gradient magnitude method and a type-1 fuzzy inference system (FIS-1). The FIS-2 results are better than the gradient magnitude and FIS-1, because the edges preserve more detail of the original images, and the backgrounds are more homogeneous than with FIS-1 and the gradient's magnitude method. © 2009 Wiley Periodicals, Inc. [source] Using soft computing techniques for improving foot trajectories in walking machinesJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 7 2001Elena Garcia Walking machines have been investigated during the last 40 years and some basic techniques of this field are already well known. However, some aspects still need to be optimized. For instance, speed seems to be one of the major shortcomings of legged robots; thus, improving leg speed has been chosen as the main aim of this work. Although some algorithms for optimizing trajectory control of robot manipulators already exist, we propose a more computationally efficient method that employs fuzzy set theory to involve real dynamic effects over leg motion instead of an inaccurate mathematical model. In this article, we improve leg speed by automatically tuning the acceleration of legs. For this purpose, we define fuzzy rules based on experiments and we find the optimal acceleration for every given trajectory. A simple fuzzy inference system is used to compute the required acceleration. It is based on five rules using three linguistic variables. Final results show that foot acceleration tuning for straight trajectory generation is a suitable method for achieving accurate, smooth and fast foot movements. Also it is shown that under some conditions average leg speed can be increased up to 100% using the control methods herein proposed. © 2001 John Wiley & Sons, Inc. [source] Anfis models for the quasistatic analysis of coplanar strip line structuresMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 9 2010Mustafa Turkmen Abstract In this work, computer-aided design models based on adaptive-network-based fuzzy inference system (ANFIS) for the quasistatic analysis of three different coplanar strip line structures are presented. These strip line structures are conventional coplanar strip lines, asymmetrical coplanar strip lines with infinitely wide strip, and asymmetrical coplanar strip lines with infinitely thick dielectric substrate. The design parameters of the proposed ANFIS models are optimally determined by using four different optimization algorithms, hybrid learning, simulated annealing, least-squares, and genetic algorithm. When the performances of ANFIS models are compared with each other, the best results for training and test are obtained from the models trained with hybrid learning algorithm. There is a good agreement among the results of ANFIS models, quasistatic analysis, a full-wave electromagnetic simulator IE3D, and experimental works realized in this study. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1990,1996, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25372 [source] Adaptive neuro-fuzzy models for the quasi-static analysis of microstrip lineMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 5 2008Celal Yildiz Abstract This article presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the effective permittivities and characteristic impedances of microstrip lines. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It has the advantages of expert knowledge of FISs and learning capability of artificial neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of ANFIS. The results of ANFIS are compared with the results of the experimental works, quasi-static methods, and a commercial electromagnetic simulator IE3D. There is very good agreement among the results of ANFIS models and quasi-static methods, IE3D, and experimental works. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1191,1196, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23322 [source] Adaptive neuro-fuzzy inference system for computing patch radius of circular microstrip antennasMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 8 2006Kerim Guney Abstract A new method based on the adaptive neuro-fuzzy inference system (ANFIS) is presented to calculate accurately the patch radius of circular microstrip antennas (MSAs). ANFIS combines the benefits of artificial neural networks (ANNs) and fuzzy inference systems (FISs) in a single model. A hybrid learning algorithm based on the least-squares method (LSM) and the backpropagation algorithm is used to identify the parameters of ANFIS. The results of ANFIS are in very good agreement with the experimental results reported elsewhere. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1606,1610, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21695 [source] Feasibility of Gait Event Detection Using Intramuscular Electromyography in the Child with Cerebral PalsyNEUROMODULATION, Issue 3 2004Richard T. Lauer PhD Abstract The objective of this study was to develop and test the feasibility of a model that employs electromyographic (EMG) signals to predict the occurrence of gait events in the child with cerebral palsy (CP). This model could be the basis of a future functional electrical stimulation (FES) control system to assist gait. Two children were implanted with bifilar intramuscular electrodes into the quadriceps muscle bilaterally. Muscle activity and gait parameters were recorded, and a fuzzy inference system was used to correlate EMG to five distinct gait events. For nine of the 10 gait events evaluated, the model predicted gait events to within 82 ms on average, as referenced to the VICON motion analysis system. For eight of the 10 events, prediction errors were 0.3% or less. Results indicate that EMG from the proximal musculature could be used to predict the occurrence of gait events in these two children with CP. [source] Neuro-fuzzy structural classification of proteins for improved protein secondary structure predictionPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 8 2003Joachim A. Hering Abstract Fourier transform infrared (FTIR) spectroscopy is a very flexible technique for characterization of protein secondary structure. Measurements can be carried out rapidly in a number of different environments based on only small quantities of proteins. For this technique to become more widely used for protein secondary structure characterization, however, further developments in methods to accurately quantify protein secondary structure are necessary. Here we propose a structural classification of proteins (SCOP) class specialized neural networks architecture combining an adaptive neuro-fuzzy inference system (ANFIS) with SCOP class specialized backpropagation neural networks for improved protein secondary structure prediction. Our study shows that proteins can be accurately classified into two main classes "all alpha proteins" and "all beta proteins" merely based on the amide I band maximum position of their FTIR spectra. ANFIS is employed to perform the classification task to demonstrate the potential of this architecture with moderately complex problems. Based on studies using a reference set of 17 proteins and an evaluation set of 4 proteins, improved predictions were achieved compared to a conventional neural network approach, where structure specialized neural networks are trained based on protein spectra of both "all alpha" and "all beta" proteins. The standard errors of prediction (SEPs) in % structure were improved by 4.05% for helix structure, by 5.91% for sheet structure, by 2.68% for turn structure, and by 2.15% for bend structure. For other structure, an increase of SEP by 2.43% was observed. Those results were confirmed by a "leave-one-out" run with the combined set of 21 FTIR spectra of proteins. [source] Design and stability discussion of an hybrid intelligent controller for an unordinary systemASIAN JOURNAL OF CONTROL, Issue 5 2009Morteza Mohammadzaheri Abstract In this paper, the pitch angle control of a lab model helicopter is discussed. This problem has some specific features. As a major unusual feature, it is observed that the steady state control command is completely dependent on the setpoint, and for different setpoints, different steady state control commands are needed to keep the error around zero. Moreover, the system is one with highly oscillating dynamics. In order to solve this control problem, two controllers are designed: an artificial neural network (ANN), whose input is the setpoint, is used to provide steady state control command, and a fuzzy inference system (FIS), whose input is error, is used to provide transient control command. The total control command is the sum of the two aforementioned control commands. It is proven that both ANN and FIS are boundary-input boundary-output (BIBO) systems. Using this fact and considering two experimental assumptions, the closed-loop stability is also proven. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Automatic diagnosis of diabetes using adaptive neuro-fuzzy inference systemsEXPERT SYSTEMS, Issue 4 2010Elif Derya Übeyli Abstract: A new approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records. The ANFIS classifier is used to detect diabetes diseases when eight features defining diabetes indications are used as inputs. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. The conclusions concerning the impacts of features on the diagnosis of diabetes disease are obtained through analysis of the ANFIS. The performance of the ANFIS model is evaluated in terms of training performances and classification accuracies and the results confirm that the proposed ANFIS model has potential in detecting diabetes diseases. [source] Complexity versus integrity solution in adaptive fuzzy-neural inference modelsINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 5 2008Georgi M. Dimirovski This paper explores aspects of computational complexity versus rule reduction and of integrity preservation versus optimality index, which have become an issue of considerable concern in learning techniques for adaptive fuzzy inference models. In control-oriented applications of adaptive fuzzy inference systems, implemented as fuzzy-neural networks, a balanced observation of these conflicting requirements appeared rather important for a good yet feasible application design. The focus is confined to a family of adaptive fuzzy inference systems that can be interpreted as a partially connected multilayer feedforward neural networks employing Gaussian activation function. The knowledge base rules are designed implying the connections are a priori fixed, and then the respective strengths adapted on the grounds of input and output data sets. Information granulation plays a significant role too. These as well as membership-function parameters ought to be adapted in a learning-training process via the minimization of an appropriate error function. © 2008 Wiley Periodicals, Inc. [source] Adaptive neuro-fuzzy inference system for computing patch radius of circular microstrip antennasMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 8 2006Kerim Guney Abstract A new method based on the adaptive neuro-fuzzy inference system (ANFIS) is presented to calculate accurately the patch radius of circular microstrip antennas (MSAs). ANFIS combines the benefits of artificial neural networks (ANNs) and fuzzy inference systems (FISs) in a single model. A hybrid learning algorithm based on the least-squares method (LSM) and the backpropagation algorithm is used to identify the parameters of ANFIS. The results of ANFIS are in very good agreement with the experimental results reported elsewhere. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1606,1610, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21695 [source] | |||||||||||||||||||