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Fuzzy Systems (fuzzy + system)

Distribution by Scientific Domains

Engineering	60%

Selected Abstracts

A Neuro-Fuzzy Logic for ATIS Stand-Alone Control Systems: Structure, Calibration, and Analyses

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2004
Yaser E. Hawas
The scheme logic attempts to optimize the network overall travel time by adjusting the path proportions while guessing the signal phase split decisions. An approximate simulation-based optimization algorithm is devised as an example of the logic operating this scheme. The logic is then replicated by a fuzzy-logic control system. Neural nets are utilized to develop the knowledge base of the fuzzy system and to calibrate the fuzzy set parameters. The neural nets utilize data replicates generated by the approximate simulation-based optimization algorithm. The calibration and effectiveness results of the fuzzy control system are presented. [source]

Hybrid expert,fuzzy approach for evaluation of complex systems

EXPERT SYSTEMS, Issue 3 2009
Veysi Öztürk
Abstract: Evaluation of complex systems is generally complicated and time consuming. Evaluation is needed for nearly all engineering tasks and the obstacles related to evaluation increase in proportion to complexity. New techniques can be used to automate manual evaluation and to overcome the obstacles related to evaluation that cannot be solved (or can only be solved with great difficulty) with conventional computing. In this study, a methodology was developed to handle the heuristic knowledge of experts for evaluation purposes. In this method, knowledge was represented as a reference model of evaluation objectives, production rules, measures, methods and parameters. A ,common evaluation process' and ,common evaluation model', which simplify and speed up the evaluation process and decrease evaluation cost, were proposed and developed. A hybrid expert,fuzzy system, called ,intelligent evaluation system' (INES), which can be used for evaluation of complex systems was developed. To define a process and develop a system that simplifies and speeds up evaluation can save time, decrease cost and provide reusability. As the evaluation of complex systems includes uncertainty in some aspects, fuzzy logic was incorporated with an expert system for reasoning. INES was implemented successfully for the evaluation of an air defence system, which is a complex system used to protect a region from all air threats. [source]

Fuzzy decision support for the control of detergent production

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8 2001
Magne Setnes
This paper describes a fuzzy decision support system (DSS) for the control of a detergent production process. The application has been carried out at a real-world, large-scale industrial production plant in the Netherlands, where a large variety of powder-based detergents for industrial users are produced in a spray drying process. The system consists of several fuzzy rule bases that model the control actions of experienced process operators in response to different quality deviations of the product. A hierarchical architecture of the fuzzy system is introduced to cope with the complexity. A fuzzy supervisor is used to deal with process constraints and to activate the applicable rule bases when control actions are needed. In this way, a system is obtained that enables the control of the process within stricter quality bounds than those applied by human operators alone. During in-production evaluation, the average improvement in the quality parameters for all product classes was above 30 percent. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Learning weighted linguistic rules to control an autonomous robot

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 3 2009
M. Mucientes
A methodology for learning behaviors in mobile robotics has been developed. It consists of a technique to automatically generate input,output data plus a genetic fuzzy system that obtains cooperative weighted rules. The advantages of our methodology over other approaches are that the designer has to choose the values of only a few parameters, the obtained controllers are general (the quality of the controller does not depend on the environment), and the learning process takes place in simulation, but the controllers work also on the real robot with good performance. The methodology has been used to learn the wall-following behavior, and the obtained controller has been tested using a Nomad 200 robot in both simulated and real environments. © 2009 Wiley Periodicals, Inc. [source]

A hybrid fuzzy-fractal approach for time series analysis and plant monitoring

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2002
Oscar Castillo
We describe in this article a new hybrid fuzzy-fractal approach for plant monitoring. We use the concept of the fractal dimension to measure the complexity of a time series of observed data from the plant. We also use fuzzy logic to represent expert knowledge on monitoring the process in the plant. In the hybrid fuzzy-fractal approach, a set of fuzzy if-then rules are used to classify different conditions of the plant. The fractal dimension is used as an input linguistic variable in the fuzzy system to improve the accuracy in the classification. An implementation of the proposed approach is shown to describe in more detail the method. © 2002 Wiley Periodicals, Inc. [source]

H, fuzzy control design of discrete-time nonlinear active fault-tolerant control systems

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 10 2009
Huai-Ning Wu
Abstract This paper is concerned with the problem of H, fuzzy controller synthesis for a class of discrete-time nonlinear active fault-tolerant control systems (AFTCSs) in a stochastic setting. The Takagi and Sugeno (T,S) fuzzy model is employed to exactly represent a nonlinear AFTCS. For this AFTCS, two random processes with Markovian transition characteristics are introduced to model the failure process of system components and the fault detection and isolation (FDI) decision process used to reconfigure the control law, respectively. The random behavior of the FDI process is conditioned on the state of the failure process. A non-parallel distributed compensation (non-PDC) scheme is adopted for the design of the fault-tolerant control laws. The resulting closed-loop fuzzy system is the one with two Markovian jump parameters. Based on a stochastic fuzzy Lyapunov function (FLF), sufficient conditions for the stochastic stability and H, disturbance attenuation of the closed-loop fuzzy system are first derived. A linear matrix inequality (LMI) approach to the fuzzy control design is then developed. Moreover, a suboptimal fault-tolerant H, fuzzy controller is given in the sense of minimizing the level of disturbance attenuation. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Robust adaptive fuzzy controller for non-affine nonlinear systems with dynamic rule activation

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 2 2003
Jang-Hyun Park
Abstract This paper describes the design of a robust adaptive fuzzy controller for an uncertain single-input single-output nonlinear dynamical systems. While most recent results on fuzzy controllers considers affine systems with fixed rule-base fuzzy systems, we propose a control scheme for non-affine nonlinear systems and a dynamic fuzzy rule activation scheme in which an appropriate number of the fuzzy rules are chosen on-line. By using the proposed scheme, we can reduce the computation time, storage space, and dynamic order of the adaptive fuzzy system without significant performance degradation. The Lyapunov synthesis approach is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as for all other signals in the closed loop. No a priori knowledge of an upper bounds on the uncertainties is required. The theoretical results are illustrated through a simulation example. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Stability analysis for discrete-time fuzzy system by utilizing homogeneous polynomial matrix function,

ASIAN JOURNAL OF CONTROL, Issue 6 2009
Likui Wang
Abstract The purpose of this paper is to investigate the stability of nonlinear systems represented by a Takagi-Sugeno discrete-time fuzzy model. The homogeneous polynomial matrix function (HPMF) is developed to obtain new stabilization conditions. Applying the HPMF to the non-parallel distributed compensation (non-PDC) law and non-quadratic Lyapunov function, some new stabilization conditions are obtained by the following two means: (a) utilizing the popular idea of introducing additional variables for some fixed degree of the HPMF; and (b) increasing the degree of the HPMF. It is shown that the conditions obtained with approach (a) are less conservative than some sufficient stability conditions available in the literature to date. It is also shown that as the degree of HPMF increases the conditions obtained under (b) become less conservative. An example is provided to illustrate how the proposed approaches compare with other techniques available in the literature. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Quantitative Evaluation of Left Ventricle Performance from Two Dimensional Echo Images

ECHOCARDIOGRAPHY, Issue 2 2006
J. Manivannan M.E.
Objectives: We sought to quantify the left ventricle systolic dysfunction by a geometric index from two-dimensional (2D) echocardiography by implementing an automated fuzzy logic edge detection algorithm for the segmentation. Background: The coronary injuries have repercussions on the left ventricle producing changes on wall contractility, the shape of the cavity, and as a whole changes on the ventricular function. Methods: 2D echocardiogram and M-mode recordings were performed over the control group and those with the dysfunctions. From 2D recordings, individual frames were extracted for at least five cardiac cycles and then segmentation of left ventricle was done by automated fuzzy systems. In each frame, the volumes are measured and a geometric index, eccentricity ratio (ER), was derived. The endocardial fractional shortening (FS), midwall fractional shortening (mFS), and the relative wall thickness (RWT) were also measured in each case. Results: Depressed value of endocardial FS (20.39 ± 5.43 vs 34.28 ± 9.36, P = 0.0046), mFS (33 ± 8.3 vs 52.5 ± 11.7, P = 0.0047), and the RWT (0.337 ± 0.096 vs 0.525 ± 0.119, P = 0.0002) was observed with dysfunction. ER measured at end-diastole (2.86 ± 0.703 vs 4.14 ± 0.38) and end-systole (3.14 ± 0.79 vs 5.48 ± 0.74) was found to be decreased in the dysfunction group and more significant at the end-systole (P = 0.00017 vs 6.6E,06). Conclusion: This work concludes that the regional and global left ventricle systolic dysfunction can be assessed by the ER measured at end-diastole and end-systole from 2D echocardiogram and may contribute to the high rate of cardiovascular disorders. [source]

Genetic fuzzy systems to evolve interaction strategies in multiagent systems

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 9 2007
Igor Walter
This article suggests an evolutionary approach to designing interaction strategies for multiagent systems, focusing on strategies modeled as fuzzy rule-based systems. The aim is to learn models evolving database and rule bases to improve agent performance when playing in a competitive environment. In competitive situations, data for learning and tuning are rare, and rule bases must jointly evolve with the databases. We introduce an evolutionary algorithm whose operators use variable length chromosomes, a hierarchical relationship among individuals through fitness, and a scheme that successively explores and exploits the search space along generations. Evolution of interaction strategies uncovers unknown and unexpected agent behaviors and allows a richer analysis of negotiation mechanisms and their role as a coordination protocol. An application concerning an electricity market illustrates the effectiveness of the approach. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 971,991, 2007. [source]

A fuzzy logic approach to experience-based reasoning

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2007
Zhaohao Sun
Experience-based reasoning (EBR) is a reasoning paradigm that has been used in almost every human activity such as business, military missions, and teaching activities since early human history. However, EBR has not been seriously studied from either a logical or mathematical viewpoint, although case-based reasoning (CBR) researchers have paid attention to EBR to some extent. This article will attempt to fill this gap by providing a unified fuzzy logic-based treatment of EBR. More specifically, this article first reviews the logical approach to EBR, in which eight different rules of inference for EBR are discussed. Then the article proposes fuzzy logic-based models to these eight different rules of inference that constitute the fundamentals for all EBR paradigms from a fuzzy logic viewpoint, and therefore will form a theoretical foundation for EBR. The proposed approach will facilitate research and development of EBR, fuzzy systems, intelligent systems, knowledge management, and experience management. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 867,889, 2007. [source]

Relevancy transformation operators: Construction methods

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 2 2006
M. Mas
This article deals with the construction of relevancy transformation (RET) operators for fuzzy systems. The notion of pseudo-duality is introduced to obtain new RET operators, and t -norms, t -conorms, nullnorms, and uninorms are used in different ways for the same purpose. Finally, several other methods to construct new RET operators from old ones are pointed out. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 155,171, 2006. [source]

Emergence of self-learning fuzzy systems by a new virus DNA,based evolutionary algorithm

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 3 2003
Lihong Ren
In this article, we propose a new approach to the virus DNA,based evolutionary algorithm (VDNA-EA) to implement self-learning of a class of Takagi-Sugeno (T-S) fuzzy controllers. The fuzzy controllers use T-S fuzzy rules with linear consequent, the generalized input fuzzy sets, Zadeh fuzzy logic and operators, and the generalized defuzzifier. The fuzzy controllers are proved to be nonlinear proportional-integral (PI) controllers with variable gains. The fuzzy rules are discovered automatically and the design parameters in the input fuzzy sets and the linear rule consequent are optimized simultaneously by the VDNA-EA. The VDNA-EA uses the VDNA encoding method that stemmed from the structure of the VDNA to encode the design parameters of the fuzzy controllers. We use the frameshift decoding method of the VDNA to decode the DNA chromosome into the design parameters of the fuzzy controllers. In addition, the gene transfer operation and bacterial mutation operation inspired by a microbial evolution phenomenon are introduced into the VDNA-EA. Moreover, frameshift mutation operations based on the DNA genetic operations are used in the VDNA-EA to add and delete adaptively fuzzy rules. Our encoding method can significantly shorten the code length of the DNA chromosomes and improve the encoding efficiency. The length of the chromosome is variable and it is easy to insert and delete parts of the chromosome. It is suitable for complex knowledge representation and is easy for the genetic operations at gene level to be introduced into the VDNA-EA. We show how to implement the new method to self-learn a T-S fuzzy controller in the control of a nonlinear system. The fuzzy controller can be constructed automatically by the VDNA-EA. Computer simulation results indicate that the new method is effective and the designed fuzzy controller is satisfactory. © 2003 Wiley Periodicals, Inc. [source]

A new universal approximation result for fuzzy systems, which reflects CNF DNF duality

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2002
Irina Perfilieva
There are two main fuzzy system methodologies for translating expert rules into a logical formula: In Mamdani's methodology, we get a DNF formula (disjunction of conjunctions), and in a methodology which uses logical implications, we get, in effect, a CNF formula (conjunction of disjunctions). For both methodologies, universal approximation results have been proven which produce, for each approximated function f(x), two different approximating relations RDNF(x, y) and RCNF(x, y). Since, in fuzzy logic, there is a known relation FCNF(x) , FDNF(x) between CNF and DNF forms of a propositional formula F, it is reasonable to expect that we would be able to prove the existence of approximations for which a similar relation RCNF(x, y) , RDNF(x, y) holds. Such existence is proved in our paper. © 2002 Wiley Periodicals, Inc. [source]

On the role of context in hierarchical fuzzy controllers

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 5 2002
Luis Magdalena
This article analyzes the role of context in hierarchical fuzzy controllers based on the decomposition of the input space. The usual consideration in most hierarchical fuzzy systems is the reduction of dimensionality problems. This article will analyze how to profit from the qualities of context as a key question in the definition of a fuzzy controller, to reduce the design efforts by making it easier to introduce the expert knowledge in that process. The idea is to use the output of a level of the hierarchy as the method to define (or adjust) the normalization functions (considered as contextual information) applied to the variables of the following level of that hierarchy. Two different situations will be analyzed, including an application example for each case. In the first case the decomposition will affect variables placed at the same level of description (abstraction) regarding the problem to be solved. In the second case, the decomposition process works on variables placed at different levels of description of the problem (descriptions with a different level of abstraction). © 2002 Wiley Periodicals, Inc. [source]

Robust control of T-S fuzzy systems with time-varying delay using new approach

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 14 2010
Hamdi Gassara
Abstract This paper aims to design a controller to robustly stabilize uncertain nonlinear systems with time-varying delay and norm bounded uncertainties via Takagi,Sugeno (T-S) fuzzy model. The stabilization conditions are given in the form of linear matrix inequalities using a single Lyapunov,Krasovskii functional (LKF) combining the introduction of some relaxation matrices and only one tuning parameter. In comparison with the existing techniques in the literature, the proposed approach has two major advantages. The first is the reduction of computational complexity when the number of IF-THEN rules, r, is big. The second concerns the conservatism reduction. Several examples are given to show the effectiveness and the merits of the design procedure. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Guaranteed cost control of T,S fuzzy systems with input delay

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 12 2008
Bing Chen
Abstract This paper is concerned with the problem of guaranteed cost control for Takagi,Sugeno (T,S) fuzzy systems with time-varying input delay. The input delay is of interval type, and no restriction is imposed on the derivative of the time delay. Based on free-weighting matrix method, new delay-dependent sufficient conditions for the existence of a fuzzy guaranteed cost controller are provided by means of linear matrix inequalities. Some examples are used to illustrate the effectiveness of the proposed method. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Robust adaptive fuzzy controller for non-affine nonlinear systems with dynamic rule activation

Control of rotational molding using adaptive fuzzy systems

ADVANCES IN POLYMER TECHNOLOGY, Issue 4 2005
D. I. Abu-Al-Nadi
Abstract Rotational molding is a method for manufacturing hollow plastic parts. In the work reported here, adaptive fuzzy logic techniques have been used to relate the machine oven temperature to other manipulated parameters of the process. The objective is to design a reliable control system for the rotational molding process. An adaptive fuzzy network was developed to correlate changes in oven temperature to changes in the opening of the control valve on the fuel system. The network parameters were optimized using real-valued genetic algorithms. This network gave good results when its performance was compared with experimental data from a commercial rotational molding machine. The network was successfully utilized to design a control system, which works well in regard to set point tracking and load rejection. © 2005 Wiley Periodicals, Inc. Adv Polym Techn 24: 266,277, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/adv.20047 [source]

H, fuzzy static output feedback control of T-S fuzzy systems based on fuzzy Lyapunov approach

ASIAN JOURNAL OF CONTROL, Issue 1 2009
Xiao-Heng Chang
Abstract This paper is concerned with the problem of H, fuzzy static output feedback control for discrete-time Takagi-Sugeno (T-S) fuzzy systems, and new design methods are presented. By defining a fuzzy Lyapunov function, a new sufficient condition guaranteeing the H, performance of the T-S fuzzy systems is derived, and the condition is expressed by a set of linear matrix inequalities. In comparison with the existing literature, the proposed approach may provide more relaxed condition while ensuring better H, performance. The simulation results illustrate the effectiveness of the proposed approach. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

An Alternative approach to H, control for fuzzy systems

ASIAN JOURNAL OF CONTROL, Issue 4 2008
Xiaodong Liu
Abstract In this paper the problem of H, dynamic feedback control for fuzzy dynamic systems has been studied. First the problem of H, dynamic feedback controller designs for complex nonlinear systems, which can be represented by Takagi-Sugeno (T-S) fuzzy systems, is presented. Second, based on a Lyapunov function, four new dynamic feedback H, fuzzy controllers are developed by adequately considering the interactions among all fuzzy sub-systems and these dynamic feedback H, controllers can be obtained by solving a set of suitable linear matrix inequalities. Finally, two examples are given to demonstrate the effectiveness of the proposed design methods. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Recursive Back-Stepping Design of An Adaptive Fuzzy Controller for Strict Output Feedback Nonlinear Systems

ASIAN JOURNAL OF CONTROL, Issue 3 2002
Wei-Yen Wang
ABSTRACT In this paper, a back-stepping adaptive fuzzy controller is proposed for strict output feedback nonlinear systems. The unknown nonlinearity and external disturbances of such systems are considered. We assume that only the output of the system is available for measurement. As a result, two filters are constructed to estimate the states of strict output feedback systems. Since fuzzy systems can uniformly approximate nonlinear continuous functions to arbitrary accuracy, the adaptive fuzzy control theory combined with a tuning function scheme is developed to derive the control laws of strict output feedback systems that possess unknown functions. Moreover, the H, performance condition is introduced to attenuate the effect of the modeling error and external disturbances. Finally, an example is simulated in order to confirm the applicability of the proposed method. [source]