Learning control using fuzzified self-organizing radial basis function network

Nie, Junhong; Linkens, D.A.

doi:10.1109/91.251928

Cited by 116 publications

(34 citation statements)

References 19 publications

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“…The most common techniques for fusion have been neural networks and fuzzy systems [5], and fuzzy systems and genetic algorithms [26]. This paper has also presented a technique for fusion of all three methodologies in a learning control context.…”

Section: Discussionmentioning

confidence: 98%

“…In spite of this, there exists a lot of similarity and a synergetic relationship between neural networks and fuzzy logic systems [3,4]. Formal equivalence between different types of fuzzy and neural systems has also been established [5], and further, it has been shown that neural networks can be constructed such that they are identical to fuzzy logic systems [6]. Both approaches build nonlinear systems based on bounded continuous variables, the difference being that neural systems are treated in a numeric, quantitative manner, whereas fuzzy systems are treated in a symbolic, qualitative manner.…”

Section: Evolutionary Neural Fuzzy Systems: An Overviewmentioning

confidence: 99%

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Enhancing neural control systems by fuzzy logic and evolutionary reinforcement

Nyongesa

1998

Neural Comput & Applic

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tation of complex nonlinear control algorithms. A persistent bottleneck, however, remains the manner in which suitable control strategies can be derived in both approaches. Each method requires optimisation of several parameters, which is usually not a trivial problem for many optimisation techniques. This paper will discuss two important issues related to rule-based fuzzy and neural systems. These issues are knowledge representation and knowledge acquisition. Knowledge representation refers to the manner in which knowledge can be expressed in connectionist, neural systems in order to preserve the advantages of fuzzy-based systems. Knowledge acquisition, on the other hand, is the manner in which the rules that represent fuzzy knowledge are obtained and stored, taking advantage of neural learning techniques. The primary objective of using fuzzy-neural approaches is to develop systems that are capable of automatic acquisition of knowledge for given network representations. In that regard, the paper will also discuss the implementation of learning using evolutionary algorithms in neuralnetwork-based fuzzy systems. Typical applications for such systems include the control of unknown nonlinear plants, a case study of which will be presented.Neural networks are widely used for system modelling and control because of their ability to approximate complex nonlinear functions [1]. Fuzzy systems, similarly, have been shown to be able to approximate or model any nonlinear system. Wang and Mendel [2], for example, showed that combinations of fuzzy membership functions are capable of approximating any continuous function to any given level of accuracy. Fuzzy-logic and neural systems, however, have very contrasting application requirements. Fuzzy systems are appropriate if sufficient expert knowledge about the process is avail-

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Section: Discussionmentioning

confidence: 98%

Section: Evolutionary Neural Fuzzy Systems: An Overviewmentioning

confidence: 99%

Enhancing neural control systems by fuzzy logic and evolutionary reinforcement

Nyongesa

1998

Neural Comput & Applic

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“…Among them, the fuzzy basis function networks (FBFNs) [8,9], which are similar in structure to radial basis function networks (RBFNs) [10][11][12][13][14], have gained much attention. In addition to their simple structure, FBFNs possess another advantage i.e., they can readily adopt various learning algorithms already developed for RBFNs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive extended fuzzy basis function network

Zhu

Cao

2006

Neural Comput & Applic

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The structure of the extended fuzzy basis function network (EFBFN) is firstly proposed, and the least squares (LS) method is used to design it by fixing the widths of the hidden units in EFBFN. Then, to enhance the performance of the obtained EFBFN ulteriorly, a novel evolutionary algorithm based on LS and the hybrid of evolutionary programming and particle swarm optimization (LS-EPPSO) is proposed, in which we use EPPSO to tune the parameters of the premise part in EFBFN, and the LS algorithm to decide the consequent parameters in it simultaneously. The enhanced EFBFN whose parameters are refined automatically using LS-EPPSO is thus called adaptive EFBFN. In the simulation part, the proposed method to construct AEFBFN is employed to model a three input nonlinear function and to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed.

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“…As will be described in the next section, weights in the fuzzification layers represent the partition of operating regions, while weights in the function layer represent the gains and time constants of local linear models. Neuro-fuzzy network representations have emerged as a powerful approach to the solution of many engineering problems [11,[13][14][15][16]. Fuzzy reasoning is capable of handling imprecise and uncertain information, whilst neural network models are capable of being identified using real plant data.…”

Section: Introductionmentioning

confidence: 99%

Long Range Predictive Control of Nonlinear Processes Based on Recurrent Neuro-Fuzzy Network Models

Zhang¹,

Morris²

2000

Neural Computing & Applications

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A recurrent neuro-fuzzy network-based nonlinear long range model predictive control strategy is proposed in this paper. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification. Based upon a neuro-fuzzy network model, a nonlinear model-based predictive controller can be developed by combining several local linear model-based predictive controllers which usually have analytical solutions. This strategy avoids the time consuming numerical optimisation procedure, and the uncertainty in convergence to the global optimum which are typically seen in conventional nonlinear modelbased predictive control strategies. Furthermore, control actions obtained based on local incremental models contain integration actions which can naturally eliminate static control offsets. The technique is demonstrated by an application to the modelling and control of liquid level in a water tank.

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Learning control using fuzzified self-organizing radial basis function network

Cited by 116 publications

References 19 publications

Enhancing neural control systems by fuzzy logic and evolutionary reinforcement

Enhancing neural control systems by fuzzy logic and evolutionary reinforcement

Adaptive extended fuzzy basis function network

Long Range Predictive Control of Nonlinear Processes Based on Recurrent Neuro-Fuzzy Network Models

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