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On the structure and learning of neural-network-based fuzzy logic control systems. (English) Zbl 0876.93059
Bien, Z. (ed.) et al., Fuzzy logic and its applications to engineering, information sciences, and intelligent systems. Proceedings of the 5th world congress of the International Fuzzy Systems Association (IFSA), Seoul, Korea, July 4–9, 1993. Dordrecht: Kluwer Academic Publishers. Theory Decis. Libr., Ser. D 16, 67-80 (1995).
Authors’ summary: This paper adresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm is proposed for constructing the NN-FLCS dynamically. The proposed dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCs) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. Associated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.
For the entire collection see [Zbl 0864.00052].
Reviewer: D.Franke (Hamburg)

93C42 Fuzzy control/observation systems
92B20 Neural networks for/in biological studies, artificial life and related topics
68T05 Learning and adaptive systems in artificial intelligence