利用模糊神經網路計算未知函數的積分

Abstract

在本論文中,我們採用模糊神經網路去學習一個未知系統的輸出輸入 關係,並求出其積分值.網路中的參數是藉由倒傳遞法則學習出來的.我們 只要對歸屬函數做積分就可達到對未知函數做積分.最後,我們將此法應用 於解決迴旋積分及常係數微分方程之問題上. In this thesis, we propose a method of usung a fuzzy neural network(FNN)system to map and compute the untegrals of an unknown function. The parameters of the FNN are learned by back-propagation algorithm. We only integrate the membership function to achieve the goal of integrating the unknown function.Finally, we apply this proposed method to solve a convolution problem and constant-coefficient differential equation problems.