A Neural Fuzzy System with Linguistic Teaching Signals

• Main Authors: Ya-Ching Lu, 呂雅菁
• Other Authors: Chin-Teng Lin
• Format: Others
• Language: en_US
• Published: 1994
• Online Access: http://ndltd.ncl.edu.tw/handle/11069725710294552970

碩士 === 國立交通大學 === 資訊科學學系 === 82 === A neural fuzzy system with linguistic teaching signals is proposed in this thesis. We use fuzzy numbers based on α-level sets to represent linguistic information. At first, we propose a five-layered neural network which can process numerical information as well as linguistic information. Moreover, the inputs and outputs of this five-layered connectionist architecture can be a hybrid of fuzzy numbers an numerical numbers. The important characteristics of the proposed model are that the network weights can be fuzzy numbers of any shaped and the performance of this model is superior to several other methods both in learning speed and memory requirement. Two kinds of learning schemes are discussed: supervised learning and reinforcement learning. With supervised learning, the proposed model can be used for rule base concentration to reduce the number of rules in knowledge base. For reinforcement learning, we consider that the reinforcement signal from environment is linguistic information such as "good" , "very good", or "bad". We discuss two kinds of reinforcement learning learning problems:single-step prediction problems and multi- step prediction problems. Simulation results of the cart- pole balancing problem are presented to illustrate the performance and applicability of the proposed reinforcement system.