摘要
在知識探勘的領域中，常見的議題包括了關連式規則探勘、分類、預測以及叢集分析，如何將大量資料進行快速的知識探勘，成為最重要的議題之一。本研究假設待實驗系統已累積相當資料於資料庫中，欲探討模糊化規則學習系統之設計，以期協助使用者在控制領域或預測領域中制定較佳與較快速之管理決策。本研究主要以Takagi-Sugeno之模糊推論為基礎發展之兩種模糊類神經網路，其一為Jang(1993)之「適應性網路架構模糊推論系統」，其二為本研究利用合理分群改善Wu(2001)四層式模糊類神經網路架構之「模糊類神經網路推論系統」，將模糊邏輯建構在類神經網路上。以此「改良式模糊類神經網路推論系統」及「適應性網路架構模糊推論系統」為基礎，先將實驗及控制特性以群集分析方法應用於歸屬函數的區分上，並能夠利用模糊邏輯的特性，以解決非確定性的數字或語義變數的問題，並寫出控制規則，同時以類神經網路解決非方程式可解問題之特性，尋找輸入變數與輸出變數間的對應關係。以倒單擺實驗、球桿系統及存貨水準控制系統等回授控制方式為例，實驗本研究之方法在模糊規則的控制下，可即時並準確地操作，進而應用本研究的方法，將其應用於分析具混沌特性之生產系統。因此，藉由預測此生產系統，建構生產模糊規則庫。經由實驗後證實，此四種推論系統均可適當的控制及預測，以改良式「模糊類神經網路推論系統」而言，為了以單階之線性關係即可解譯性之基礎下呈現規則，則在其精簡之架構背後，則會降低其對系統推論之準確度，而「適應性網路架構模糊推論系統」實驗可得到較精準的控制，可同時驅動多條規則成為單階非線性之控制，但並無法針對每筆控制寫成簡易規則，則為此兩種系統互有之優缺點。
並列摘要
Fuzzy Neural Networks have been successfully applied to extract knowledge from data in the form of fuzzy rules. However, the drawback with the fuzzy neural approach is that the fuzzy rules induced by the learning process are not necessarily understandable. The purpose of this thesis is thus to improve and evaluate two kinds of fuzzy neural network based on Takagi-Sugeno fuzzy inference system. Specificially speaking, this study investigates the adaptive network-based fuzzy inference system (ANFIS) and an improved the fuzzy nueral network (FNN). The proposed FNN uses fuzzy c-means for clustering data set, while most of the fuzzy neural networks including the ANFIS, use the sum of all existing rules as output. The first-order rule is utilized in the FNN. The proposed models are applied to a feedback control system and an empirical study with four cases is conducted. The four experiments are (i) a cart and pole system, (ii) a ball and beam system, (iii) a chaos production forecasting system and (iv) an inventory control system. Experiment outcomes revealed that the two models can precisely generate human-understandable fuzzy rules with good interpretability. The advantage and disadvantage of the FNN and ANFIS are discussed as well.