內嵌粒子群優化學習演算法之類神經模糊系統及其應用

Abstract

本篇論文中所提出的進化式神經模糊系統乃是採用內嵌以粒子群為基礎的學習演算法之函數鏈結類神經模糊網路(Functional-Link-Based Neuro-Fuzzy Network, FLNFN)。此一類神經模糊網路採用函數鏈結類神經網路來做為模糊法則的後件部。由於，後件部採用了非線性函數展開的方式，來形成任意複雜的決策邊界。因此，在FLNFN模型中，後件部的這個局部特性，可以使輸入變量的非線性組合結果，能夠更有效地近似目標輸出。本論文主要為三大部分。在第一部份，我們提出了一個高效率的免疫粒子群優化（IPSO）的學習方法來解決膚色檢測的問題。我們所提的免疫粒子群優化演算法主要是結合免疫演算法（IA）和粒子群優化（PSO）來進行參數學習。在第二部分中，另一種被稱為細菌覓食粒子群優化（BFPSO）的混合式參數學習演算法，將被介紹來解決分類的應用。BFPSO演算法主要是透過BFO的趨化運動來操作執行區域性的搜索，而在整個搜索空間的全域搜索則是由PSO來完成。利用此一方式，便能在全域性的勘探和區域性的開採間取得最好的平衡。在第三部分中，與先前採用混合方法不同，我們引入了以距離為基礎的突變操作元，藉以用來增加粒子群的群體多樣性。此演算法包含架構學習及參數學習兩部分。架構學習是藉由熵的量測來決定所需的模糊法則的數目。參數學習則是使用內嵌以距離為基礎的突變操作元之粒子群優化演算法（DMPSO），來調整歸屬函數的形狀與後件部的相對應權重。最後，我們將論文中所提出的以PSO為基礎之學習演算法應用到各種分類和控制問題。本論文的實驗結果證明了所提出方法的有效性。

This dissertation proposes the evolutionary neural fuzzy system, designed using functional-link-based neuro-fuzzy network (FLNFN) model embedded with PSO-based learning algorithms. The FLNFN model uses a functional link neural network to the consequent part of the fuzzy rules. The consequent part uses a nonlinear functional expansion to form arbitrarily complex decision boundaries. Thus, the local properties of the consequent part in the FLNFN model enable a nonlinear combination of input variables to be approximated more effectively. This dissertation consists of three major parts. In the first part, the efficient immune-based particle swarm optimization (IPSO) learning method is presented to solve the skin color detection problem. The proposed IPSO algorithm combines the immune algorithm (IA) and particle swarm optimization (PSO) to perform parameter learning. In the second part, another hybrid parameter learning algorithm, called bacterial foraging particle swarm optimization (BFPSO), is introduced for classification applications. The proposed BFPSO algorithm performs local search through the chemotactic movement operation of BFO whereas the global search over the entire search space is accomplished by a PSO operator. In this way it balances between exploration and exploitation enjoying best of both the worlds. In the third part, instead of using hybrid techniques, the distance-based mutation operator is introduced to improve the population diversity. The learning algorithm consists of structure learning and parameter learning. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on distance-based mutation particle swarm optimization (DMPSO), can adjust the shape of the membership function and the corresponding weights of the consequent part. Finally, the proposed PSO-based learning algorithms are applied in various classification and control problems. Results of this dissertation demonstrate the effectiveness of the proposed methods.