臺灣博碩士論文加值系統

摘要
在這份論文中，我們整合了CMAC (小腦模型控制器) 與SVR (支持向量回歸) 技術以發展一個更有效率與方法的架構，CMAC擁有一些引人注意的特性，它極快的學習能力與其特別的架構有利於我們有效的將它用於數位硬體的實現。SVR是一個基於統計學習定理的新式方法用來針對函數近似及回歸估測的問題；同時，SVR對於雜訊也顯露出不錯的強健特性。在論文中，我們結合SVM與CMAC (CMAC-GBF)來建構一個基於SVR的CMAC (CMAC-GBF)系統。模擬的結果我們可以發現它是一個高精確與高效率的系統，更重要的是基於SVR的CMAC (CMAC-GBF)系統在表現上優於單純的CMAC系統。

Abstract
In this thesis, we integrate the techniques of cerebellar model articulation controller (CMAC) and support vector regression (SVR) to develop a more efficient scheme. In general, CMAC has some attractive features. The most important ones are its extremely fast learning capability and the special architecture that lets effective digital hardware implementation as possible. On the other hand, a SVR is a novel method for tackling the problems of function approximation and regression estimation based on the statistical learning theory and has robust properties that against noise. In this thesis, we propose the SVR-based CMAC (CMAC-GBF) systems that combined by SVR and CMAC (CMAC-GBF) systems. From the results of simulation, the proposed structure has high accuracy and noise against. Besides, the experimental testing results demonstrate that the SVR-based CMAC (CMAC-GBF) systems outperform the original CMAC (CMAC-GBF) systems.

Contents
誌謝 i
摘要 ii
Abstract iii
Contents iv
List of Figure v
List of Table vii
Chapter 1 Introduction 1
Chapter 2 Overview of the CMAC and CMAC-GBF systems 5
2-1. Conventional Cerebellar Model Articulation Controller (CMAC) 5
2-2. Overview of the CMAC-GBF systems 7
Chapter 3 Embedded Support Vector Regression on CMAC and CMAC-GBF 10
3-1. Embedded Support Vector Regression (SVR) on CMAC 10
3-2. Embedded Support Vector Regression (SVR) on CMAC-GBF 13
Chapter 4 Simulation of SVR-CMAC and SVR based CMAC-GBF 17
4-1. Simulation Results for SVR-CMAC 17
4-2. Simulation Results for SVR based CMAC-GBF systems 27
Chapter 4 Conclusion 38
Reference 39

Reference

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