Title :
A neural fuzzy system with linguistic teaching signals
Author :
Lin, Chin-Teng ; Lu, Ya-Ching
Author_Institution :
Dept. of Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fDate :
5/1/1995 12:00:00 AM
Abstract :
A neural fuzzy system learning with linguistic teaching signals is proposed. This system is able to process and learn numerical information as well as linguistic information. It can be used either as an adaptive fuzzy expert system or as an adaptive fuzzy controller. First, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use α-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, two kinds of learning schemes are developed for the proposed system: fuzzy supervised learning and fuzzy reinforcement learning. Simulation results are presented to illustrate the performance and applicability of the proposed system
Keywords :
computational linguistics; expert systems; feedforward neural nets; fuzzy control; fuzzy logic; fuzzy neural nets; fuzzy systems; inference mechanisms; learning (artificial intelligence); adaptive fuzzy controller; adaptive fuzzy expert system; connectionist structure; five-layered neural network; fuzzy inference system; fuzzy logic rules; fuzzy reinforcement learning; fuzzy supervised learning; interval arithmetics; linguistic teaching signals; membership functions; neural fuzzy system; Adaptive control; Adaptive systems; Control systems; Education; Fuzzy control; Fuzzy logic; Fuzzy systems; Hybrid intelligent systems; Neural networks; Programmable control;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
