A Reinforcement Learning Algorithm for Fuzzy Neural Network with Distributed Prediction Scheme

• Main Authors: Wang ,Cheng-Wen, 王正文
• Other Authors: Yau-Hwang Kuo
• Format: Others
• Language: en_US
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/46323056531384654117

碩士 === 國立成功大學 === 資訊及電子工程研究所 === 83 === This thesis proposes a reinforcement learning algorithm for fuzzy neural network with distributed prediction scheme (RFNN- DPS). The proposed RFNN-DPS model is constructed with a multilayered fuzzy neural network model. It doesn't require another neural network predictor for predicting the external reinforcement signal, and the internal reinforcement information is distributed into every fuzzy rule. In other words, the proposed model uses only a network to construct the system and equips itself with the functions of the action network and the prediction network. The credit vector of rules can predict the external reinforcement signal and provide a more profitable internal reinforcement signal to themselves. The RFNN-DPS performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. The number of rules can be adjusted dynamically, and the membership functions of input/output variables can be learned automatically and incrementally. The RFNN-DPS model operates on an on-line learning mode, and two algorithms which are the single-step reinforcement learning algorithm and the multi-step reinforcement learning algorithm. After the proposed model is trained, a network structuralized to represent a set of fuzzy rules retrieved in the reinforcement learning process can be achieved. In summary, RFNN-DPS model has the advantages of single model structure, fast learning speed, retrieving the reliability of rules, and simple learning algorithm.