基於強化式學習架構下使用FP-growth基因演算法建構模糊類神經控制系統

Abstract

近年來進化式演算法在各個領域中的應用非常普遍，目前傳統基因演算法在處理染色體時是使用隨機選取的方式來進行交配和突變，可能會重覆調整巳經有好的效能的基因，而造成演化時間過長，故希望利用資料探勘尋找關聯資料的能力，提出一個方法使我們能找出改善適應值高相關的基因位置；有系統的找到合適的交配點和突變點使得演 算法收斂的更快；演算法更有效率。 本篇論文提出一個架構，使用強化式學習機制建構模糊類神經控制系統，在學習中使用資料探勘的FP-growth演算法來提高基因演算法的演化效率，並提出非對稱性的交配和突變動作來學習模糊類神經網路控制器的參數與架構。且利用球桿系統和混沌系統的控制問題為例，測試本系統達成控制目標與學習的功能。由模擬結果得知，本論文所提出演算法可達到滿意的結果。

Recently, evolutionary algorithms are widely applied in several regions. In the traditionalgenetic algorithm, the chromosome is evolved by using random search to execute crossover and mutation. However, the gene with good performance may be tuned repeatedly and the evolutionary time will be much longer. In this thesis, a genetic algorithm based on data mining is adopted to solve this problem. By using the ability of looking for association data, the gene point associating with the improvement of fitness value can be found. Hence, suitable crossover points and mutation points can be found systematically, and the algorithm can be converged more efficiently. In this thesis, a neural-fuzzy control system using reinforcement learning is constructed. This thesis also uses the technique of data mining to enhance the evolution efficiency of the algorithm. The asymmetric crossover and mutation is proposed to learn the parameters and structure of neural-fuzzy control system. The ball and beam control system and chaos control system are used as examples to test learning ability and controllability of the proposed system. The experimental results show the system is satisfactory.