具有微分進化演算法的模糊類神經網路之研究

此篇論文題出一個經由微分進化演算法的模糊類神經網路控制器，命名為微分進化模糊類神經網路控制器。所提出的微分進化模糊類神經網路控制器是由模糊類神經辨識器，微分進化估測器，計算控制器以及迫近控制器所組成的。微分進化模糊類神經網路控制器主要分為兩個階段-訓練階段和線上階段。訓練階段是被利用來搜尋微分進化模糊類神經網路控制器最好的預設參數。這篇論文中，這些預設參數經由微分進化估測器來訓練，分別是倒傳遞演算法的學習率，使用在倒傳遞演算法中的誤差項參數，模糊類神經網路辨識器的初始值以及一些預設參數。當最好的預設參數被獲得以後，微分進化模糊類神經網路控制器就可以在線上運行。在線上階段中，類神經模糊網路辨識器被使用來辨證非線性系統的動態未知項。而倒傳遞演算法被採用來更新模糊類神經網路辨識器的參數來達到有利的近似特性。接著計算控制器被設計來計算模糊類神經網路辨識器的輸出。最後，迫近控制力，被利用來估測非線性系統的不確定項和外部干擾，與計算控制器的輸出結合成主要的控制力。模擬結果被應用來證實所提出控制器的有效性。

關鍵字

微分進化演算法；辨識器；倒單擺；模糊類神經網路

並列摘要

A differential evolution (DE) algorithm based fuzzy neural network (FNN) (DEFNN) controller is proposed in this thesis. DEFNN controller is composed of an FNN identifier, a DE estimator, a computation controller, and a hitting controller. There are two main learning phases in DEFNN controller – the training phase and the online phase. The training phase is utilized to find the best preset parameters of DEFNN controller. In this thesis, several parameters such as the learning rates of the back-propagation (BP) algorithm, the parameters of error term which are used in BP algorithm, the initial values of the FNN identifier and some preset parameters of DEFNN controller are provided by DE estimator. After the best preset parameters are obtained, DEFNN controller will be active online. In the online phase, the FNN identifier is used to identify the unknown terms of the nonlinear system dynamic. The BP algorithm is adopted to update the parameters of the FNN identifier to achieve favorable approximation performance. Then the computation controller is designed to calculate the outputs of the FNN identifier. Finally, the hitting control which is utilized to eliminate the uncertainties and external disturbances of the nonlinear system combine with the output of computation controller to form the main control effort. The results of the simulations are implemented to verify the effectiveness of the proposed controller.

並列關鍵字

identifier ； fuzzy neural network ； inverted pendulum ； differential evolution

參考文獻

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具有微分進化演算法的模糊類神經網路之研究

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