资源列表
chap4
- 自适应RBF神经网络控制，包括神经网络逼近的自适应控制和参数未知的自适应控制。-Adaptive RBF neural network control, adaptive control and adaptive control of unknown parameters including the neural network approximation.
6
- 基于模型整体逼近的自适应RBF控制，包括基于HJI理论和RBF神经网络的鲁棒控制-Adaptive RBF control based on the approximation of the model, including the robust HJI control theory and based on RBF neural network
chap
- 基于局部逼近的自适应RBF控制，主要包括基于局部模型逼近的自适应RBF机械手控制。-Adaptive RBF control based on the local approach, including adaptive RBF manipulator control model based on the local approach.
lms
- 基于LMS算法的自适应回波抵消系统。 可应用与无线直放站回波抵消算法，改善25DB。 也应用与噪声抵消系统。-base on lms arithmetic A Radio Repeater Interference Cancellation Model for MobileCommunication Systems
exp_pid
- 专家pid控制的阶跃响应，能够很好的跟踪输入信号。具有良好的性能指标。-step response of Expert pid control , and can be very good to track the input signal.With good performance。
FUZZY_PID
- 模糊PID控制的阶跃响应，并与PID控制进行对比，验证了模糊PID控制的有效性。-The step response of fuzzy PID control, and compared with PID control, verify the effectiveness of the fuzzy PID control.
Classification_LS_SVMlab
- 基于SVM的分类器 支持两类分类 和多类分类选择最优参数-SVM-based classifier supports two types of classification and multi-class classification
Classification_OSU_SVM
- 基于SVM的分类器 支持两类分类 和多类分类-SVM-based classifier supports two types of classification and multi-class classification
Classification_SVM_SteveGunn
- 基于SVM的分类器 支持两类分类 和多类分类 选择最优参数 -Select the optimal parameters of the classifier based on SVM supports two types of classification and multi-class classification
Regression_LS_SVMlab
- 基于SVM的分类器 支持两类分类 和多类分类 选择最优参数-Select the optimal parameters of the classifier based on SVM supports two types of classification and multi-class classification
Regression_SVM_SteveGunn
- 基于SVM的分类器 支持两类分类 和多类分类 选择最优参数-Select the optimal parameters of the classifier based on SVM supports two types of classification and multi-class classification
c45
- 决策树算法c4.5 C语言实现以及命名规则和构建工具开发集合-C4.5 decision tree algorithm C language, naming and build tools set