搜索资源列表
RBF-
- 基于RBF网络逼近的自适应控制,内部包含三个m文件和一个simulink的文件,可以直接运行。-Based on RBF network approximation of adaptive control, interior contains three m file and a simulink file can be run directly.
RBF-network-sliding
- 基于RBF网络补偿的控制输入受限滑模控制-Compensation control based on RBF network sliding mode control input constraints
085321
- 基于模糊神经网络模型,RBF神经网络控制研究 -RBF Control Research based on Fuzzy Neural Model
RBF-inverted-pendulum-system
- 利用RBF神经网络对倒立摆系统进行控制,仿真证明能够对倒立摆的位置和速度进行很好的跟踪-By using RBF neural network to control the inverted pendulum system, the simulation prove that the position and speed of the inverted pendulum can be a good tracking
PID-Control-Based-on-RBF
- PID Control Based on RBF Neural Network for Ship Steering-A PID control combined with Radial Basis Function (RBF) neural network was proposed for course control of ship steering.
RBF-network-control
- RBF神经网络自适应控制,matlab仿真-RBF network control
rbf
- RBF是一种径向基神经网络,可用于神经网络的建模,控制等各个方面-RBF is a kind of RBF neural network, neural network can be used for modeling, control and so on various aspects
RBF-NEURAL-CONTROOL
- RBF神经网络自适应控制MATLAB仿真代码-RBF neural control
RBF
- RBF神经网络:rbf原理:所谓径向基函数(Radial Basis Function 简称 RBF),就是某种沿径向对称的标量函数。通常定义为空间中任一点x到某一中心xc之间欧氏距离的单调函数,可记作 k(||x-xc||),其作用往往是局部的,即当x远离xc时函数取值很小。最常用的径向基函数是高斯核函数,形式为 k(||x-xc||)=exp{- ||x-xc||^2/(2*σ)^2) } 其中xc为核函数中心,σ为函数的宽度参数,控制了函数的径向作用范围。在RBF网络中,这两个参数往往是可
RBF.tar
- radial basis function network is an artificial neural network that uses radial basis functions a ctivation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis functi
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- 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.
RBF
- 针对一类非线性动态系统给出了一种基于RBF(径向基函数)神经网络的模型参考自适应控制算法,控制器的结构中使用RBF网络来动态的补偿系统的非线-A class of nonlinear dynamic systems presents a model reference adaptive RBF (Radial Basis Function) neural network control algorithms, structure of the controller using RBF netwo
RBF
- 基于神经网络控制的三容水箱仿真、运用RBF网络进行编程-Three- tank control based on neural network simulation using RBF network programming
matlab-RBF
- RBF网络能够逼近任意的非线性函数,可以处理系统内的难以解析的规律性,具有良好的泛化能力,并有很快的学习收敛速度,已成功应用于非线性函数逼近、时间序列分析、数据分类、模式识别、信息处理、图像处理、系统建模、控制和故障诊断等。-RBF network can approximate any nonlinear function, can handle regular system are difficult to resolve, with good generalization ability,
RBF-sliding-mode-control
- 使用matlab编程,控制对象为二阶倒立摆,控制率为RBF滑膜控制,对模型所得数据绘图,可以实现较好的鲁棒性-Using matlab programming, the second-order inverted pendulum control object, control rate RBF synovial control, drawing on the resulting data model, we can achieve better robustness
RBF
- 针对传统的PID控制器参数固 定而导致在控制中效果差的问题,提出一种基于模糊RBF神经网络智能PID控制器的设计方法。该方法结合了模糊控制的推理能力强与神经网络学习能力强的特 点,将模糊控制与RBF神经网络相结合以在线调整PID控制器参数,整定出一组适合于控制对象的kp,ki,kd参数。将算法运用到电机控制系统的PID 参数寻优中,仿真结果表明基于此算法设计的PID控制器改善了电机控制系统的动态性能和稳定性。-Traditional PID controller parameters fixed
RBF-neural-network-nonlinear-system
- RBF神经网络非线性系统的输出反馈控制,及英语原文资料。-Output Feedback Control of RBF neural network nonlinear system, and the English original data.
第11章 模糊RBF网络
- 模糊控制器的设计不依靠被控对象的模型,而是依靠控制专家或操作者的经验知识。模糊控制的突出优点是能够比较容易地将人的控制经验溶入到控制器中,但若缺乏一定的控制经验,很难设计出高水平的模糊控制器。而且,由于模糊控制器采用了IF-THRN控制规则,不便于控制参数的学习和调整,使得构造具有自适应的模糊控制器较困难(The design of fuzzy controller does not depend on the model of the controlled object, but depend
RBF_PID
- RBF神经网络算法,通过MATLAB软件实现神经网络控制算法(RBF neural network algorithm, through MATLAB software to achieve neural network control algorithm)