Construction of Fuzzy Inference System with AI-Neuro Based Autonomous Learning Abilities and Verification of Its Effectiveness

点击翻译按钮获取中文摘要

From constraint-oriented perspectives, a new concept of fuzziness is introduced, and based on this concept, new method of fuzzy inference is formalized, which is then shown to be able to be coupled tightly with symbolic procesing in AI that is based two-valued logic. This new method of combined inference is then applied to sensing information processing and image understanding under incomplete information, and also to planning and its execution of autonomous mobile robot. Moreover, the combined inference is applied to control of inverted pendulum, which elucidates its effectiveness. Also, new programming environment for this inference system is introduced by using CLP(R), a kind of constraint logic programming language. By using machine learning techniques, a learning method for acquiring fuzzy inference rules is introduced, whose results are then refined and organized by neural networks and genetic algorithms. Finally, the total system for fuzzy inference with automomous learning abilities is applied to control and planning of autonomous mobile robots, which elucidates the effectiveness of our approach.