適應模糊系統之理論與應用研究

Abstract

本文針對模糊系統發展出含有參數學習和結構調整的競爭學習法則及 教導學習法則，所探討的內容著重在設計的基本原理、運算特性以及模糊 系統本身的適應性。為了實現適應性於模糊系統中，我們建議把模糊系統 中的邏輯法則予以參數化，同時簡化模糊系統的運算模式。根據競爭學習 的基本原理，我們提出參數與結構競爭學習法則使模糊系統能處理分類與 分群問題。依照應用環境的特性，我們列舉數種教導學習法則，使模糊系 統在設計上更加方便。最後，我們發展一個能夠視實際問題需要而自動改 變法則數目的結構學習理論。經由實例模擬證明，我們所提出競爭學習和 教導學習的參數及結構調整方法，對於解決近似函數、樣本分類、向量量 化、資料分群及 系統識別問題皆相當有效。 This thesis presents appropriate model structures for fuzzy systems, and accompanies these model structures with parameter-level learning and structure-level learning. The emphasis of the present exercise is on basic principles of the design, operating characteristics, and adaptation of fuzzy systems. In order to incorporate adaptation into fuzzy systems, a refined mathematical model format for fuzzy systems is developed in such a way that the fuzzy logical rules in the systems are parameterized. Two general learning paradigms are considered: competitive learning and supervised learning. By incorporating competitive learning into fuzzy system, we demonstrate that fuzzy systems can be used effectively for categorization and clustering of unlabeled input patterns. Methods for dynamically adjusting the parameters and structures based on fuzzy competitive learning are discussed. To facilitate system design, we present several supervised learning algorithms for adjusting parameters. Also, a novel structure-level supervised learning algorithm that is able to self-organize the number of fuzzy rules is proposed. The results of simulations reveal that the proposed parameter-level as well as structure- level competitive learning and supervised learning algorithms are practically feasible. Potential applications include function approximation, pattern classification, vector quantization, clustering, and system identification.