機械系統RBF神經網絡控制：設計、分析及MATLAB仿真（簡體書）

《機械系統RBF神經網絡控制：設計、分析及Matlab仿真（英文）》從Matlab仿真角度，結合典型機械系統控制的實例，系統地介紹了神經網絡控制的基本理論、基本方法和應用技術，是作者多年來從事控制系統教學和科研工作的結晶，同時融入了國內外同行近年來所取得的新成果。
全書共分11章，包括RBF網絡的設計及分析、基於梯度下降法的RBF網絡控制、簡單的RBF網絡自適應控制、RBF網絡滑模控制、基於RBF網絡逼近的自適應控制、基於RBF網絡的自適應反演控制、RBF網絡數字控制、離散系統的RBF網絡控制及自適應RBF網絡觀測器的設計。每種控制方法都通過Matlab進行了仿真分析。
《機械系統RBF神經網絡控制：設計、分析及Matlab仿真（英文）》各部分內容既相互聯繫又相互獨立。《機械系統RBF神經網絡控制：設計、分析及Matlab仿真（英文）》適用於從事生產過程自動化、計算機應用、機械電子和電氣自動化領域工作的工程技術人員閱讀，也可作為大專院校工業自動化、自動控制、機械電子、自動化儀錶、計算機應用等專業的教學參考書。

1 Introduction
1.1 Neural Network Control
1.1.1 Why Neural Network Control?
1.1.2 Review of Neural Network Control
1.1.3 Review of RBF Adaptive Control
1.2 Review of RBF Neural Network
1.3 RBF Adaptive Control for Robot Manipulators
1.4 S Function Design for Control System
1.4.1 S Function Introduction
1.4.2 Basic Parameters in S Function
1.4.3 Examples
1.5 An Example of a Simple Adaptive Control System
1.5.1 System Description
1.5.2 Adaptive Control Law Design
1.5.3 Simulation Example
Appendix
References

2 RBF Neural Network Design and Simulation
2.1 RBF Neural Network Design and Simulation
2.1.1 RBF Algorithm
2.1.2 RBF Design Example with Matlab Simulation
2.2 RBF Neural Network Approximation Based on Gradient Descent Method
2.2.1 RBF Neural Network Approximation
2.2.2 Simulation Example
2.3 Effect of Gaussian Function Parameters on RBF Approximation
2.4 Effect of Hidden Nets Number on RBF Approximation
2.5 RBF Neural Network Training for System Modeling
2.5.1 RBF Neural Network Training
2.5.2 Simulation Example
2.6 RBF Neural Network Approximation
Appendix
References

3 RBF Neural Network Control Based on Gradient Descent Algorithm
3.1 Supervisory Control Based on RBF Neural Network
3.1.1 RBF Supervisory Control
3.1.2 Simulation Example
3.2 RBFNN-Based Model Reference Adaptive Control
3.2.1 Controller Design
3.2.2 Simulation Example
3.3 RBF Self-Adjust Control
3.3.1 System Description
3.3.2 RBF Controller Design
3.3.3 Simulation Example
Appendix
References

4 Adaptive RBF Neural Network Control
4.1 Adaptive Control Based on Neural Approximation
4.1.1 Problem Description
4.1.2 Adaptive RBF Controller Design
4.1.3 Simulation Examples
4.2 Adaptive Control Based on Neural Approximation with Unknown Parameter
4.2.1 Problem Description
4.2.2 Adaptive Controller Design
4.2.3 Simulation Examples
4.3 A Direct Method for Robust Adaptive Control by RBF
4.3.1 System Description
4.3.2 Desired Feedback Control and Function Approximation
4.3.3 Controller Design and Performance Analysis
4.3.4 Simulation Example
Appendix
References

5 Neural Network Sliding Mode Control
5.1 Typical Sliding Mode Controller Design
5.2 Sliding Mode Control Based on RBF for Second-Order SISO Nonlinear System
5.2.1 Problem Description
……

6 Adaptive RBF Control Based on Global Approximation
7 Adaptive Robust RBF Control Based on Local Approximation
8 Backstepping Control with RBF
9 Digital RBF Neural Network Control
10 Discrete Neural Network Control
11 Adaptive RBF Observer Design and Sliding Mode Control
Index