ScholarBank@NUShttps://scholarbank.nus.edu.sgThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Wed, 03 Mar 2021 06:34:34 GMT2021-03-03T06:34:34Z50251- Stochastic topology with elastic matching for off-line handwritten character recognitionhttps://scholarbank.nus.edu.sg/handle/10635/111212Title: Stochastic topology with elastic matching for off-line handwritten character recognition
Authors: Lim, J.H.; Teh, H.H.; Lui, H.C.; Wang, P.Z.
Abstract: We propose a novel approach called Stochastic Topology with Elastic Matching (STEM) for off-line handwritten character recognition. Fitting characters as topological maps, STEM incrementally learns stochastic prototypes from examples with elastic matching. Experimental results on NIST digit database and connection to deformable models are also presented.
Thu, 08 Feb 1996 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1112121996-02-08T00:00:00Z
- The min-max function differentiation and training of fuzzy neural networkshttps://scholarbank.nus.edu.sg/handle/10635/62861Title: The min-max function differentiation and training of fuzzy neural networks
Authors: Zhang, X.; Hang, C.-C.; Tan, S.; Wang, P.-Z.
Abstract: This paper discusses the Δ-rule and training of min-max neural networks by developing a differentiation theory for min-max functions, the functions containing min (∧) and/or max (∨) operations. We first prove that under certain conditions all min-max functions are continuously differentiable almost everywhere in the real number field R-fraktur sign and derive the explicit formulas for the differentiation. These results are the basis for developing the Δ-rule for the training of min-max neural networks. The convergence of the new Δ-rule is proved theoretically using the stochastic theory, and is demonstrated with a simulation example. © 1996 IEEE.
Mon, 01 Jan 1996 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/628611996-01-01T00:00:00Z
- Fuzzy Set Operations Based on the Theory of Falling Shadowshttps://scholarbank.nus.edu.sg/handle/10635/117024Title: Fuzzy Set Operations Based on the Theory of Falling Shadows
Authors: Tan, S.K.; Wang, P.Z.; Lee, E.S.
Abstract: In this paper, we establish a theoretical approach to define fuzzy set operations based on the theory of falling shadows. The main characteristic of our definition of fuzzy set operations is that it is semantically dependent in the sense that the formula of our definition will vary according to the correlation of the fuzzy sets concerned. We show that the well known formulae of fuzzy set operations such as the max-mm operations, the bounded sum-difference operations, and the probability sum-product operations are consequences of our definition under three different correlations of the fuzzy sets. © 1993 Academic Press. All rights reserved.
Mon, 15 Mar 1993 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1170241993-03-15T00:00:00Z
- Control of dynamical processes using an on-line rule-adaptive fuzzy control systemhttps://scholarbank.nus.edu.sg/handle/10635/61971Title: Control of dynamical processes using an on-line rule-adaptive fuzzy control system
Authors: He, S.-Z.; Tan, S.; Hang, C.-C.; Wang, P.-Z.
Abstract: In this paper, we present a detailed discussion on the design of a rule-adaptive fuzzy control system, along with simulation analysis demonstrating its usefulness in controlling plants of various dynamical natures. Conceived first by Wang, and further clarified in [8], the rule-adaptive fuzzy controller considered here generates the fuzzy control signal as a convex combination of the standard fuzzy inputs to the controller, the error and the error rate. Such a combination is automatically adjusted on-line in response to the varying control situations with certain updating scheme. This paper considers two different updating schemes. The first one is based on a fuzzy relationship, and is a revision of the rule presented in [11]. The present paper also proposes the second updating rule that is essentially a nonlinear differential equation. Both of the rules are applied to the simulations of several selected dynamical plants. The simulation results show that the controller is effective in controlling a wide range of dynamical plants. Especially, it shows better performance in controlling the plants found difficult to be controlled by the conventional means. The simulation also shows that the controller can be used to curb the effect of load disturbance in the control process. © 1993.
Thu, 25 Feb 1993 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/619711993-02-25T00:00:00Z
- Self-tuning adaptive resolution (STAR) fuzzy control algorithmhttps://scholarbank.nus.edu.sg/handle/10635/111284Title: Self-tuning adaptive resolution (STAR) fuzzy control algorithm
Authors: Lui, Ho Chung; Gu, Ming Kun; Goh, Tiong Hwee; Wang, Pei Zhuang
Abstract: A novel Self-Tuning Adaptive Resolution (STAR) fuzzy control algorithm is introduced in this paper. One of the unique features is that the fuzzy linguistic concepts change constantly in response to the states of input signals. This is achieved by modifying the corresponding membership functions. We use this adaptive resolution capability to realize a control strategy that attempts to minimize both the rise time and the overshoot. Simulation results on a simple inverted pendulum problem are presented. Its characteristics are compared with the classical PD controller. Finally, the algorithm is also realized to control a real inverted pendulum hardware. Experimental results show that the STAR controller is both robust and can minimize positional error with drastically reduced overshoot.
Sat, 01 Jan 1994 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1112841994-01-01T00:00:00Z
- Sequential representation of fuzzy similarity relationshttps://scholarbank.nus.edu.sg/handle/10635/111210Title: Sequential representation of fuzzy similarity relations
Authors: Tan, S.K.; Teh, H.H.; Wang, P.Z.
Abstract: Using a matrix to represent a fuzzy similarity relation has commonly been used in the study of fuzzy relations. Here, we introduce an efficient and compact way to represent similarity relations in the form of a sequence. We shall firstly introduce the notion of the normal form of a membership matrix and then establish an algorithm to obtain the normal form of any given membership matrix of a similarity relation. From the normal form of the matrix, a sequential representation of the similarity relation can be constructed. We shall further discuss the relationship between the matrix representation and the sequential representation of fuzzy similarity relations. Both the algorithm and the construction method will be illustrated by examples. © 1994.
Mon, 24 Oct 1994 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1112101994-10-24T00:00:00Z
- On random sets, knowledge acquisition, and pattern recognitionhttps://scholarbank.nus.edu.sg/handle/10635/111196Title: On random sets, knowledge acquisition, and pattern recognition
Authors: Peng, X.T.; Wang, P.; Kandel, A.
Abstract: In this article, we investigate knowledge acquisition and pattern recognition by using the principles of random sets. Based on random set theory, we develop some estimation theorems and procedures for set-valued statistics, such as nonparametric estimators and set-valuedization techniques. Under random interval assumption, we establish some special possibility distributions that can be easily implemented in knowledge acquisition tools. The knowledge studied here are rules describing relationships between various concepts, as used in diagnosis (pattern recognition) expert systems. Several examples are given to illustrate the estimation theorems and procedures for the acquisition of concepts and relationships. We use our acquisition techniques on a modeling prediction example in two different ways: One is by acquiring the concepts and relationships simultaneously; another is by acquiring rules for predefined concepts. On two classification problems, we use our methods to acquire classification rules. The results are compared with several machine learning methods. © 1996 John Wiley & Sons, Inc.
Fri, 01 Mar 1996 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1111961996-03-01T00:00:00Z
- A stock selection strategy using fuzzy neural networkshttps://scholarbank.nus.edu.sg/handle/10635/111130Title: A stock selection strategy using fuzzy neural networks
Authors: Wong, F.S.; Wang, P.Z.; Teh, H.H.
Abstract: This paper describes, from a general system-design perspective, an artificial neural network (ANN) approach to a stock selection strategy. The paper suggests a concept of neural gates which are similar to the processing elements in ANN, but generalized into handling various types of information such as fuzzy logic, probabilistic and Boolean information together. Forecasting of stock market returns, assessing of country risk and rating of stocks based on fuzzy rules, probabilistic and Boolean data can be done using the proposed neural gates. Fuzzy logic is known to be useful for decision-making where there is a great deal of uncertainty as well as vague phenomena, but lacks the learning capability; on the other hand, neural networks are useful in constructing an adaptive system which can learn from historical data, but are not able to process ambiguous rules and probabilistic data sets. This paper describes how these problems can be solved using the proposed neural gates. © 1991 Kluwer Academic Publishers.
Wed, 01 May 1991 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1111301991-05-01T00:00:00Z
- A prolog-like inference system based on neural logic - An attempt towards fuzzy neural logic programminghttps://scholarbank.nus.edu.sg/handle/10635/111129Title: A prolog-like inference system based on neural logic - An attempt towards fuzzy neural logic programming
Authors: Ding, L.; Teh, H.H.; Wang, P.; Lui, H.C.
Abstract: Research under the name of Neural Logic Networks is an attempt to integrate connectionist models and logic reasoning [8, 9]. With a Neural Logic Network, a simple neural network structure with suitable weight(s) can be used to represent a set of flexible operations, which offer increased possibilities in dealing with inference in real-world problem solving. They also possess useful properties in an extended logic system which is called Neural Logic. One of the important features of Neural Logic is that all its operations can be defined and realized by neural networks, which form Neural Logic Networks. As one part of the research on Neural Logic Networks, fuzzy neural logic programming has been proposed [6]. This paper introduces a Prolog-like inference system based on Neural Logic as an implementation of fuzzy neural logic programming. In this system, fuzzy reasoning is executed by the Neural Logic inference engine with incomplete or uncertain knowledge. The framework of the system and its inference mechanism are described. Copyright © 1996 Elsevier Science B.V. All rights reserved.
Mon, 01 Jan 1996 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1111291996-01-01T00:00:00Z
- Framework for integrating fault diagnosis and incremental knowledge acquisition in connectionist expert systemshttps://scholarbank.nus.edu.sg/handle/10635/111253Title: Framework for integrating fault diagnosis and incremental knowledge acquisition in connectionist expert systems
Authors: Lim, Joo-Hwee; Lui, Ho-Chung; Wang, Pei-Zhuang
Abstract: In this paper, we propose a framework for integrating fault diagnosis and incremental knowledge acquisition in connectionist expert systems. A new case solved by the Diagnostic Function is formulated as a new example for the Learning Function to learn incrementally. The Diagnostic Function is composed of a neural networks-based Example Module and a symbolic-based Rule Module. While the Example Module is always first invoked to provide the shortcut solution the Rule Module provides extensive coverage of eases to handle odd cases when Example Module fails. Two applications based on the proposed framework will also be briefly mentioned.
Wed, 01 Jan 1992 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1112531992-01-01T00:00:00Z
- Cascade architecture of adaptive fuzzy controllers for inverted pendulumshttps://scholarbank.nus.edu.sg/handle/10635/111237Title: Cascade architecture of adaptive fuzzy controllers for inverted pendulums
Authors: Gu, Ming Kun; Lui, Ho Chung; Goh, Tiong Hwee; Wang, Pei Zhuang
Abstract: This paper presents a cascade architecture design for the control of an inverted pendulum. A novel self-tuning adaptive resolution (STAR) fuzzy control approach is implemented in this control design. The controller consists of two sub-controllers: one of which is used to control the small cart's position and the other is used to balance the pole. Because of the unique architecture, the controller shows very good stability and robustness even under disturbances and uneven load conditions. The STAR fuzzy control provides excellent performances in controlling the cart's position and balancing the pole. In order to compare the performance of the STAR approach, a conventional fuzzy control method has also been implemented to control the same inverted pendulum.
Sat, 01 Jan 1994 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1112371994-01-01T00:00:00Z
- Mathematical theory of truth-valued flow inferencehttps://scholarbank.nus.edu.sg/handle/10635/111189Title: Mathematical theory of truth-valued flow inference
Authors: Wang, P.Z.; Zhang, X.H.; Lui, H.C.; Zhang, H.M.; Xu, W.
Abstract: Inference problems are one of the main research topics in the AI field. So far there have been proposed various inference systems some of which have been applied in various problems according to their feature. In particular, the concepts of inference channel and truth-valued flow inference (TVFI) (Wang, 1988) have been used in building fuzzy inference machines. In this paper, we discuss the basic concepts of TVFI channel lattice, background graph, the confidence degree of channels, and knowledge combination, etc. © 1995.
Fri, 09 Jun 1995 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1111891995-06-09T00:00:00Z
- Knowledge acquisition by random setshttps://scholarbank.nus.edu.sg/handle/10635/111188Title: Knowledge acquisition by random sets
Authors: Peng, X.T.; Wang, P.; Kandel, A.
Abstract: In this article we investigate knowledge acquisition (KA) and its relationships to random sets. Based on random set theory, we develop some estimation theorems and procedures for set-valued statistics such as nonparametric estimators. Under random interval assumption, we establish some special possibility distributions that can be easily implemented in KA tools. The knowledge studied here are rules describing relationships between various concepts, as used in diagnosis (pattern recognition) expert systems. © 1996 John Wiley & Sons, Inc.
Fri, 01 Mar 1996 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1111881996-03-01T00:00:00Z
- Mathematical theory of inference channelshttps://scholarbank.nus.edu.sg/handle/10635/111264Title: Mathematical theory of inference channels
Authors: Tan, Sie-Keng; Wang, Pei-Zhuang
Abstract: In this paper, we introduce the notion of inference channels and consider their truth values flowing among these channels. We shall study the mathematical structure of these inference channels based on a framework of knowledge representation and thus establish a theory of inference to gain an insight into the core of approximate reasoning.
Fri, 01 Jan 1993 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1112641993-01-01T00:00:00Z
- Delta rule and learning for min-max neural networkshttps://scholarbank.nus.edu.sg/handle/10635/81395Title: Delta rule and learning for min-max neural networks
Authors: Zhang, Xinghu; Hang, Chang-Chieh; Tan, Shaohua; Wang, Pei-Zhuang
Abstract: There have been a lot of works discussing (V, Λ)-neural network (see references). However, because of the difficulty of mathematical analysis for (V, Λ)-functions, most previous works choose bounded-plus (+) and multiply (*) as the operations of V and Λ. The (V, Λ) neural network with operators (+, *) is much easier than the (V, Λ) neural network with some other operators, e.g. min-max operators, because it has only a little difference from Back-propagation Neural Network. In this paper, we choose min and max as the operations of V and Λ. Because of the difficulty of functions involved with min and max operations, it is much difficult to deal with (V, Λ) neural network with operators (min, max). In Section 1 of this paper, we first discuss the differentiations of (V, Λ)-functions, and get that 'if f1(x), f2(x), ..., fn(x) are continuously differentiable in real number line R, then any function h(x) generated from f1(x), f2(x), ..., fn(x) through finite times of (V, Λ) operations is continuously differentiable almost everywhere in R'. This statement guarantee that the Delta Rule given in Section 2 is rational and effective. In Section 3 we implement a simple example to show that the Delta Rule given in Section 2 is capable to train (V, Λ) neural networks.
Sat, 01 Jan 1994 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/813951994-01-01T00:00:00Z
- Fuzzy identification and controller based on generalized fuzzy radial basis function networkshttps://scholarbank.nus.edu.sg/handle/10635/81431Title: Fuzzy identification and controller based on generalized fuzzy radial basis function networks
Authors: Zhang, Xinghu; Hang, Chang-Chieh; Tan, Shaohua; Wang, Pei-Zhuang
Abstract: This paper first proposes a new kind of fuzzy neural networks - Generalized Fuzzy Radial Basis Function Networks (f-RBF), which combines the fuzzifying and defuzzifying processes into a united network structure. We then give the dynamic training rule and training strategy for the f-RBF. We further discuss several special features of this kind of networks that conventional neural networks do not have, and conclude that it can process both the fuzzy-valued and real-valued data simultaneously, and can achieve the minimum realization of fuzzy controller for nonlinear systems. Finally, using the f-RBF, we design a fuzzy controller for a nonlinear system regulation. Furthermore, we point out that any nonlinear control u can be decomposed into three parts: a fuzzy control uf, a linear control ul, and an error compensation ue, i.e., u = uf + ul + ue. The stability of the closed-loop system is also analyzed using sliding control techniques.
Sun, 01 Jan 1995 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/814311995-01-01T00:00:00Z
- On an objective-centered adaptive fuzzy control methodologyhttps://scholarbank.nus.edu.sg/handle/10635/81624Title: On an objective-centered adaptive fuzzy control methodology
Authors: Tan, Shaohua; Lin, Yu; Pok, Yang-Ming; Wang, Pei-Zhuang
Abstract: This paper presents a new adaptive fuzzy control scheme that is formulated and constructed directly in the control objective space. The formulation on the basis of decomposition of closed-loop response profile is clarified first followed by a detailed description of the scheme. Unlike the existing adaptive fuzzy control methods, the fuzzy controller in the new scheme is fixed and the adaptation is done on the input and output weighting factors of the fuzzy controller. A state-space based approximate analysis technique is employed to analyze the stability of the closed-loop system. A simulation analysis is also conducted to evaluate the controller performance in regulating a structure varying process, and to illustrate the advantage of the scheme in controlling plants that can not be easily handled by other control approaches.
Sat, 01 Jan 1994 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/816241994-01-01T00:00:00Z
- The min-max function differentiation and training of fuzzy neural networkshttps://scholarbank.nus.edu.sg/handle/10635/81253Title: The min-max function differentiation and training of fuzzy neural networks
Authors: Zhang, X.; Hang, C.-C.; Tan, S.; Wang, P.-Z.
Abstract: This paper discusses the Δ-rule and training of min-max neural networks by developing a differentiation theory for min-max functions, the functions containing min (∧) and/or max (∨) operations. We first prove that under certain conditions all min-max functions are continuously differentiable almost everywhere in the real number field R-fraktur sign and derive the explicit formulas for the differentiation. These results are the basis for developing the Δ-rule for the training of min-max neural networks. The convergence of the new Δ-rule is proved theoretically using the stochastic theory, and is demonstrated with a simulation example. © 1996 IEEE.
Mon, 01 Jan 1996 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/812531996-01-01T00:00:00Z
- Constructive theory for fuzzy systemshttps://scholarbank.nus.edu.sg/handle/10635/50543Title: Constructive theory for fuzzy systems
Authors: Wang, P.-Z.; Tan, S.; Song, F.; Liang, P.
Abstract: In this paper, a constructive theory is developed to establish the fact that we can build a fuzzy system to approximate any continuous function on a compact set within a prescribed error bound. Based on the theory, an algorithm is described that can actually construct a near minimum fuzzy system for a given function to a desired accuracy. © 1997 Elsevier Science B.V.
Wed, 01 Jan 1997 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/505431997-01-01T00:00:00Z
- Building fuzzy graphs from samples of nonlinear functionshttps://scholarbank.nus.edu.sg/handle/10635/50537Title: Building fuzzy graphs from samples of nonlinear functions
Authors: Tan, S.; Yu, Y.; Wang, P.-Z.
Abstract: This paper considers the problem of constructing fuzzy graphs from samples of multi-dimensional nonlinear functions to meet a precision requirement. It starts by defining the basic notions of fuzzy granulation, fuzzy graph, fuzzification and defuzzification. The formulation of the problem is then stated in a fuzzy granulation and function approximation framework. This is followed by the development of a recursive scheme that builds a fuzzy graph by generating a set of fuzzy rules and membership functions from the samples of a nonlinear function. Rigorous analysis is carried out to establish the convergence of such a recursive scheme. A few illustrative examples are also used to assess both the efficacy and the efficiency of the proposed scheme. © 1998 Elsevier Science B.V.
Thu, 01 Jan 1998 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/505371998-01-01T00:00:00Z
- Fuzzy self-tuning of PID controllershttps://scholarbank.nus.edu.sg/handle/10635/80481Title: Fuzzy self-tuning of PID controllers
Authors: He, S.-Z.; Tan, S.; Xu, F.-L.; Wang, P.-Z.
Abstract: This paper presents a novel fuzzy self-tuning PID control scheme for regulating industrial processes. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a single parameter α, then to use an on-line fuzzy inference mechanism to self-tune the parameter. The fuzzy tuning mechanism, with process output error and error rate as its inputs, adjusts α in such a way that it speeds up the convergence of the process output to a set-point yr, and slows down the divergence trend of the output from yr. A comparative simulation study on various processes, including a second-order process, processes with long dead-time and non-minimum phase processes, shows that the performance of the new scheme improves considerably, in terms of set-point and load disturbance responses, over the PID controllers well-tuned using both the classical Ziegler-Nichols formula and the more recent Refined Ziegler-Nichols formula. © 1993.
Tue, 25 May 1993 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/804811993-05-25T00:00:00Z
- A rule self-regulating fuzzy controllerhttps://scholarbank.nus.edu.sg/handle/10635/116910Title: A rule self-regulating fuzzy controller
Authors: Qiao, W.Z.; Zhuang, W.P.; Heng, T.H.; Shan, S.S.
Abstract: The most effective way to improve the performance of a fuzzy controller may be to optimize the fuzzy control rules. Usually fuzzy control rules are generated by: (1) translating the operators' experience into fuzzy linguistic form directly; (2) monitoring and summarizing the control behaviour of the operators; (3) modelling the process to be controlled using fuzzy set theory; (4) self organizing in running of the control systems. Different from the above means, the authors in [10] have proposed a method to regulate fuzzy control rules where the control rules are represented by an analytic expression with a regulating factor α. The authors in this paper employ such a method and use a simplified fuzzy control algorithm which allows fuzzy control rules to be regulated on line, thus constructing a Rule Self-regulating Fuzzy Controller (RSFC). Simulation results demonstrate that the performance of RSFC is better than that of a non-rule-regulating fuzzy controller. © 1992.
Fri, 10 Apr 1992 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1169101992-04-10T00:00:00Z
- A combinatoric formulahttps://scholarbank.nus.edu.sg/handle/10635/102619Title: A combinatoric formula
Authors: Wang, P.Z.; Lee, E.S.; Tan, S.K.
Sun, 15 Sep 1991 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/1026191991-09-15T00:00:00Z
- Fuzzy inference relation based on the theory of falling shadowshttps://scholarbank.nus.edu.sg/handle/10635/52951Title: Fuzzy inference relation based on the theory of falling shadows
Authors: Tan, S.K.; Wang, P.Z.; Zhang, X.Z.
Abstract: In this paper, we establish a theoretical approach to define a fuzzy inference relation based on the theory of falling shadows. The main characteristic of our definition of fuzzy inference relation is that it is semantically dependent in the sense that the formula of our definition will vary according to the correlation of the antecedent and the consequence of the given implication. We shall show that the formulae of fuzzy inference relation given by Łukasiewicz, Zadeh and the probability formula are consequences of our definition under three different correlations of the propositions. © 1993.
Mon, 25 Jan 1993 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/529511993-01-25T00:00:00Z
- On an objective-centered adaptive fuzzy control methodologyhttps://scholarbank.nus.edu.sg/handle/10635/72817Title: On an objective-centered adaptive fuzzy control methodology
Authors: Tan, Shaohua; Lin, Yu; Pok, Yang-Ming; Wang, Pei-Zhuang
Abstract: This paper presents a new adaptive fuzzy control scheme that is formulated and constructed directly in the control objective space. The formulation on the basis of decomposition of closed-loop response profile is clarified first followed by a detailed description of the scheme. Unlike the existing adaptive fuzzy control methods, the fuzzy controller in the new scheme is fixed and the adaptation is done on the input and output weighting factors of the fuzzy controller. A state-space based approximate analysis technique is employed to analyze the stability of the closed-loop system. A simulation analysis is also conducted to evaluate the controller performance in regulating a structure varying process, and to illustrate the advantage of the scheme in controlling plants that can not be easily handled by other control approaches.
Sat, 01 Jan 1994 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/728171994-01-01T00:00:00Z