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Supervisory control of fuzzy discrete event systems: A formal approach
 IEEE Trans. Syst., Man, Cybern., Part B
, 2005
"... 1 Abstract: Fuzzy discrete event systems (DESs) were proposed recently by Lin and Ying [19], which may better cope with the realworld problems with fuzziness, impreciseness, and subjectivity such as those in biomedicine. As a continuation of [19], in this paper we further develop fuzzy DESs by deal ..."
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Cited by 12 (2 self)
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1 Abstract: Fuzzy discrete event systems (DESs) were proposed recently by Lin and Ying [19], which may better cope with the realworld problems with fuzziness, impreciseness, and subjectivity such as those in biomedicine. As a continuation of [19], in this paper we further develop fuzzy DESs by dealing with supervisory control of fuzzy DESs. More specifically, (i) we reformulate the parallel composition of crisp DESs, and then define the parallel composition of fuzzy DESs that is equivalent to that in [19]; maxproduct and maxmin automata for modeling fuzzy DESs are considered; (ii) we deal with a number of fundamental problems regarding supervisory control of fuzzy DESs, particularly demonstrate controllability theorem and nonblocking controllability theorem of fuzzy DESs, and thus present the conditions for the existence of supervisors in fuzzy DESs; (iii) we analyze the complexity for presenting a uniform criterion to test the fuzzy controllability condition of fuzzy DESs modeled by maxproduct automata; in particular, we present in detail a general computing method for checking whether or not the fuzzy controllability condition holds, if maxmin automata are used to model fuzzy DESs, and by means of this method we can search for all possible fuzzy states reachable from initial fuzzy state in maxmin automata; also, we introduce the fuzzy ncontrollability condition for some practical problems; (iv) a number of examples serving to illustrate the applications of the derived results and methods are described; some basic properties related to supervisory control of fuzzy DESs are investigated. To conclude, some related issues are raised for further consideration.
Fuzzy automaton induction using neural network
 Int. J. Approximate Reasoning
, 2001
"... It has been shown that neural networks are able to infer regular crisp grammars from positive and negative examples. The fuzzy grammatical inference �FGI) problem however has received considerably less attention. In this paper we show that a suitable twolayer neural network model is able to infer f ..."
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Cited by 4 (1 self)
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It has been shown that neural networks are able to infer regular crisp grammars from positive and negative examples. The fuzzy grammatical inference �FGI) problem however has received considerably less attention. In this paper we show that a suitable twolayer neural network model is able to infer fuzzy regular grammars from a set of fuzzy examples belonging to a fuzzy language. Once the network has been trained, we develop methods to extract a deterministic representation of the fuzzy automaton encoded in the network that recognizes the training set. Ó 2001Elsevier Science Inc. All rights reserved.
A new approach to knowledgebased design of recurrent neural networks
 IEEE Trans. Neural Networks
, 2008
"... Abstract — A major drawback of artificial neural networks (ANNs) is their blackbox character. This is especially true for recurrent neural networks (RNNs) because of their intricate feedback connections. In particular, given a problem and some initial information concerning its solution, it is not ..."
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Cited by 3 (3 self)
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Abstract — A major drawback of artificial neural networks (ANNs) is their blackbox character. This is especially true for recurrent neural networks (RNNs) because of their intricate feedback connections. In particular, given a problem and some initial information concerning its solution, it is not at all clear how to design an RNN that is suitable for solving this problem. In this paper, we consider a fuzzy rulebase with a special structure, referred to as the fuzzy allpermutations rulebase (FARB). Inferring the FARB yields an inputoutput mapping that is mathematically equivalent to that of an RNN. We use this equivalence to develop two new knowledgebased design methods for RNNs. The first method, referred to as the direct approach, is based on stating the desired functioning of the RNN in terms of several sets of symbolic rules, each one corresponding to a subnetwork. Each set is then transformed into a suitable FARB. The second method is based on first using the direct approach to design a library of simple modules, such as counters or comparators, and realize them using RNNs. Once designed, the correctness of each RNN can be verified. Then, the initial design problem is solved by using these basic modules as building blocks. This yields a modular and systematic approach for knowledgebased design of RNNs. We demonstrate the efficiency of these approaches by designing RNNs that recognize both regular and nonregular formal languages.
Building Subcomponents in the Cooperative Coevolution Framework for Training Recurrent Neural Networks
, 2009
"... Cooperative coevolution decomposes a large problem into its subcomponents and uses evolutionary algorithms for solving them in order to gradually solve the large problem. This paper uses cooperative coevolution framework for training recurrent neural networks for grammatical inference problems. In t ..."
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Cited by 2 (1 self)
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Cooperative coevolution decomposes a large problem into its subcomponents and uses evolutionary algorithms for solving them in order to gradually solve the large problem. This paper uses cooperative coevolution framework for training recurrent neural networks for grammatical inference problems. In the past, different encoding schemes were used to build subcomponents from the neural network for the cooperative coevolution framework. This work proposes a new encoding scheme for building subcomponents which is based on the functional properties of a neuron and compares it with the best encoding scheme from literature. All subcomponents in their respective cooperative coevolution framework employ the G3PCX evolutionary algorithm. The results show the the proposed encoding scheme for building subcomponents achieves better performance, although, it has a lower level of modularity when compared to the CC framework used from literature. The level of modularity of the proposed encoding scheme further enables it to have smaller number of function evaluations in the initialisation stage when compared to their previous counterparts. The approach is further used for longterm dependency problems and demonstrates to learn from strings lengths of up to 500 time lags.