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The subject of this book covers three different specific academic areas:
Nonlinear systems, adaptive filtering and system identification.
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nonlinear system identification using neural networks on matlab
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OgunfunmT.Adaptive nonlinear system identification
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基于CMAC的非线性系统动态辨识，选并联辨识的机构-Based on the CMAC nonlinear system dynamic identification, chooses the parallel identification the organization
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基于Elman网络的非线性系统辨识，选并联辨识结构-Based on the Elman network nonlinear system identification, chooses the parallel identification structure
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基于Hopfield网络的非线性系统辨识，含６个神经元-Based on Hopfield network nonlinear system identification, including 6 neurons
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关于神经网络非线性系统的辨识，模糊控制器等程序。-With regard to neural network, nonlinear system identification, fuzzy controller and other procedures.
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【1】随机序列产生程序
【2】白噪声产生程序
【3】M序列产生程序
【4】二阶系统一次性完成最小二乘辨识程序
【5】实际压力系统的最小二乘辨识程序
【6】递推的最小二乘辨识程序
【7】增广的最小二乘辨识程序
【8】梯度校正的最小二乘辨识程序
【9】递推的极大似然辨识程序
【10】Bayes辨识程序
【11】改进的神经网络MBP算法对噪声系统辨识程序
【12】多维非线性函数辨识程序的Matlab程序
【13】模糊神经网络解耦M
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Developing Models from Experimental Data using System Identification Toolbox-1. webinar_walk_through.m: contains all the linear and nonlinear estimation examples presented during the webinar.
2. Data files and Simulink models: process_data.mat,
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这是一个用matlab编写的，基于CAMC，pid神经网络的非线性系统识别程序，能够很好的识别非线性系统-Based on CAMC/PID Nonlinear System Identification
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在MATLAB运行环境下，基于支持向量机的非线性系统辨识程序！-Run in the MATLAB environment, based on support vector machines for nonlinear system identification procedure!
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This addresses the use of ANFIS function in the Fuzzy Logic Toolbox for nonlinear dynamical system identification. This
also requires the System Identification Toolbox, as a comparison is made between a nonlinear ANFIS and a linear ARX model.
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基于神经网络在线辨识的自适应逆振动控制技术。可以有效地应用到非线性系统的控制。-Line identification based on neural network adaptive inverse vibration control technology. Can be effectively applied to nonlinear system control.
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激光焊接系统的非线性辨识和自适应控制Laser welding system identification and adaptive control of nonlinear-Laser welding system identification and adaptive control of nonlinear
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换档品质评价方法的研究是应现代车辆自动变速技术发展需要而提出的前沿研究课
题,其评价过程可视为一个非线性动态系统。BP 神经网络的非线性系统辨识 ,能够逼近任意
一个非线性函数。通过确定换档品质评定指标 ,利用BP网络训练获得的数据样本 ,从而建立
主观、 客观评价标准之间的联系 利用 Mat lab/ Simulink 完成换档品质评价方法的客观描述并
进行仿真。通过与实验结果对比 ,证明这种方法能够有效真实地评价换档品质并与传统主观
评价方法具有很好的一致性。-Shift
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Nonlinear System Identification
This demo addresses the use of ANFIS function in the
Fuzzy Logic Toolbox(TM) for nonlinear dynamical system identification.
This demo also requires the System Identification Toolbox(TM), as a comparison is
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Nonlinear System Identification: A State-Space Approach
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This code for identification of nonlinear system using neural networks, as an example we use a SISO system
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非线性系统辨识 的matlab的仿真模型
-Nonlinear System Identification
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基于BP算法的神经网络对一个非线性系统进行辨识，最终辨识达到稳定并有较小的辨识误差。-BP neural network algorithm is based on a nonlinear system identification, and ultimately to identify stable and has a small identification error.
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