利用粒子群優演算法改善模糊知識之整合; Improving Fuzzy Knowledge Integration with Particle Swarm Optimization

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Title:	利用粒子群優演算法改善模糊知識之整合;Improving Fuzzy Knowledge Integration with Particle Swarm Optimization
Authors:	李育峰;Yu-feng Lee
Contributors:	資訊工程學系碩士在職專班
Keywords:	粒子群智慧;swarm intelligence
Date:	2009-12-25
Issue Date:	2010-06-11 16:18:45 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	在此論文中，我們提出了一基於粒子群優演算法的模糊知識整合方法，可用於多個模糊知識庫的融合之用。本研究將可增進整合後知識庫的準確率及降低其規則複雜度。所提出的方法包含了兩階段程序：一是演化式的模糊知識編碼，二是基於粒子群優演算法的知識融合階段。在編碼階段中，每個模糊規則集及其相對應的歸屬函數將被編碼置於同一字串並構成初始的知識粒子群。融合階段中，將利用粒子群優演算法來探尋出最佳化或接近最佳化的模糊規則與其歸屬函數。我們將其應用於學生程式學習樣式診斷及適性化學習服務組合這兩個領域，並展示出我們所提出的知識整合方法的效率。實驗的結果可顯示出我們的系統能有效的提高整合後的知識庫規則準確率及可降低其規則複雜度。將有助於知識推論及決策制定之有效進行。 This paper presents an approach to integrate multiple fuzzy knowledge bases for increasing the accuracy and decreasing the complexity of the integrated knowledge base. The proposed approach consists of two phases: PSO-based fuzzy knowledge encoding, and PSO-based fuzzy knowledge fusion. In the encoding phase, the fuzzy rule sets and fuzzy sets with its corresponding membership functions are encoded as a string and are put together in the initial knowledge population. In the fusion phase, the particle swarm algorithm is used to explore the fuzzy rule sets, fuzzy sets and membership functions to its optimal or the approximately optimal extent. Two application domains, including diagnosis on student’s program learning style and situational learning services composition, were used to demonstrate the performance of the proposed knowledge integration approach. Experiment results revealed that our approach will effectively increase the accuracy and decrease the complexity of integrated knowledge base. The results of this study could extend the effectiveness of knowledge inference and decision making.
Appears in Collections:	[Executive Master of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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