摘要
本文以適應性基因演算法來設計最佳遞迴模糊類神經網路控制器。遞迴模糊類神經網路控制器，具有回饋連結來代表記憶元件，並以一廣義的動態倒傳遞演算法線上調整其參數值。通常遞迴模糊類神經網路控制器學習速率及參數的初值，是以隨機方式或依經驗來產生，如此將會造成人力資源的浪費且效率不彰；因此本文使用適應性基因演算法來進行其最佳化設計。適應性基因演算法是以適應函數值的高低來動態調整交配率及突變率，如此能加速收斂並減少落入區域解的機會。將所設計最佳遞迴模糊類神經網路控制器分別應用於模擬二階線性、非線性及高度非線性有瞬間負載等系統的控制且與未最佳化之RFNNC做比較。 模擬結果顯示，對於最佳化設計而言，學習速率就如同模糊參數一般，是項重要因素；而此最佳設計的確使每個模擬系統都達到最低的誤差平方和，且設計過程可完全由電腦程式自動完成。
並列摘要
In this thesis, an optimal recurrent fuzzy neural network controller is by an adaptive genetic algorithm. The recurrent fuzzy neural network has recurrent connections representing memory elements and uses a generalized dynamic backpropagation algoruthm to adjust fuzzy parameters on-line. Usually, the learning rate and the initial parameter values are chosen randomly or by experience, therefore is human resources consuming and inefficient. An adaptive genetic algorithm is used instead to optimize them. The adaptive genetic algorithm adjust the probability of crossover and mutation adaptively according to fitness values, therefore can avoid falling into local optimum and speed up convergence. The optimal recurrent fuzzy neural network controller is applied to the simulation of a second-ordeer linear system, a nonlinear system, a highly nonlinear system with instantaneous loads. The simulation results show that the learning rate as well as other fuzzy parameters are important factor for the optimal design. Certainly, with the optimal design, every simulation achieve the lowest sum of squared error and the design process done automatically by computer programs.