具有相互影響之遞迴式自我演化類神經模糊系統及其應用

Abstract

本篇論文提出以相互影響地遞迴式架構為基礎之類神經模糊系統及其應用於動態系統辨識。而此論文主要分成四大部份, 第二部份詳細介紹相互影響地遞迴式架構與類神經模糊系統作結合，並且在後鑑部份使用輸入變數的非線性組合，不同於傳統Takagi-Sugeno-Kang （TSK）架構，它是利用函數展開的方式，能在高維度的輸入空間中提供良好的非線性決策能力，因此，可使網路輸出更具體且更逼近目標輸出。在架構學習上，使用線上學習模糊分群法，而此演算法能有效地處理時變特徵問題。在參數學習上，後鑑部參數的更新是由可變動維度之卡爾曼濾波器演算法調整,可具有高精密學習的性能。前鑑部及遞迴式參數則利用梯度下降法去做參數更新的動作。在第三部份中，我們提出區間第二類型模糊邏輯系統結合發展出的遞迴式網路，即相互影響地遞迴式區間第二類型類神經模糊系統。區間第二類型模糊邏輯系統具有良好的雜訊容忍度，能直接處理規則的不確定性，這是第一類型模糊邏輯系統所不能達到的。在架構學習上，相互影響之遞迴式區間第二類型類神經模糊系統最初不包含任何規則，所有規則的產生是由線上第二類型模糊分群所取得。在參數學習上，後鑑部參數的更新是由排序後規則之卡爾曼濾波器演算法調整以改善系統的性能。前鑑部及遞迴參數的更新由梯度下降法做調整。在此，我們提出參數消除的方法針對無效的遞迴參數，因規則數太大，會產生許多遞迴參數，我們將冗餘的遞迴參數刪除，來減少網路的計算量。模擬結果顯示出針對動態系統在無喧雜的狀況下，所提出的相互影響之遞迴式模糊類神經網路具有優越的性能。最後，我們將與其他方法做比較，證實所提出的架構是卓越的。

This dissertation mainly describes two different kinds of recurrent neural fuzzy systems, involving a novel recurrent self-evolving fuzzy neural network for identification and prediction of time-varying plants and a novel recurrent interval type-2 neural fuzzy system with self-evolving structure and parameter for dynamic system processing under noise-free and noise environment. For the first kind, we describe a novel recurrent self-evolving neural fuzzy system, namely an interactively recurrent self-evolving fuzzy neural network (IRSFNN). The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN can be chosen by a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNN’s learning starts with an empty rule base and all of rules are generated and learned online through a simultaneous structure and parameter learning. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results. For the second kind, we introduce a mutually recurrent interval type-2 neural fuzzy system (MRIT2NFS) with self-evolving structure and parameters for system identification under both noise-free and noisy environments. The MRIT2NFS employs interval type-2 set in the premise clause in order to enhance noise tolerance of system. The consequent part of each recurrent fuzzy rule is defined using the Takagi-Sugeno-Kang (TSK) type with interval weights. The structure learning of MRIT2NFS uses on-line type-2 fuzzy clustering to determine number of fuzzy rules. For parameter learning, the consequent part parameters are tuned by rule-ordered Kalman filter algorithm to reinforce parameter learning ability. The type-2 fuzzy sets in the antecedent and weights representing the mutual feedback are learned by gradient descent algorithm. After the training, a weight-elimination scheme eliminates feedback connections that do not have much effect on the network behavior. This method can efficiently remove redundant recurrence weights. Simulation results show that the MRIT2NFS produces smaller root mean squared errors using the same number of iterations.