Description: 提出了一种基于小波神经网络整定的PID 控制方法。由于小波变换具有良
好的时频局部特性,神经网络具有强大的非线性映射能力,自学习、自适应等优势,采用规
范正交的小波函数作为神经网络的基函数构成小波神经网络,该网络兼有小波函数的紧
支性、波动性以及神经网络的非线性映射能力,自学习、自适应能力等优点,渗碳炉控制实
验结果表明,用该方法整定的PID 控制系统收敛速度快,逼近精度高,鲁棒性好-Based on wavelet neural network-tuning of PID control methods. Since the wavelet transform has good time-frequency localization properties, neural network has strong ability of nonlinear mapping, self-learning, adaptive and other advantages, the use of standardized orthogonal wavelet function as a neural network constitutes a wavelet basis function neural network, the network a combination of compactly supported wavelet function, and volatility as well as the neural network nonlinear mapping ability, self-learning, adaptive capacity, etc., carburizing furnace control experimental results show that using this method of tuning PID control system for fast convergence approximation of high accuracy, good robustness Platform: |
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Author:guole |
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Description: BP神经网络已广泛应用于非线性建摸、函数逼近、系统辨识等方面，但对实际问题，其模型结构需由
实验确定，无规律可寻。简要介绍了利用 Matlab语言进行 BP网络建立、训练、仿真的方法及注意事项。 -BP neural network has been widely used in nonlinear modeling, function approximation, system identification, etc., but the practical problems, the model structure required by the experiment, if there is no law to be found. Briefly introduce the use of Matlab language BP networks, training, simulation methods and Cautions. Platform: |
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Author:覃亮朋 |
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Description: These set of programs are written to design a robust power system stabilizer for minimizing the effects of low frequency oscillations in electric power systems. A complete nonlinear model of the power system represented by a single machine connected to an infinite bus is developed in the Simulink environment. A fuzzy Power System Stabilizer is designed using the fuzzy logic toolbox of matlab and its parameters are tuned by a NEural Network controller. Platform: |
Size: 15360 |
Author:al-amin |
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Description: 【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Matlab程序
【14】F-检验法部分程序
-【1】 【2-random sequence generation process white noise generation process】 【3】 M sequence generation process 【4】 to complete a one-time second-order system least-squares identification procedure 【5】 actual pressure system least-squares identification procedure 【6】 Delivery Push the least squares identification procedure augmented 【7】 【8】 least square identification procedures for gradient correction least square identification procedure 【9】 Recursive maximum likelihood identification procedures 【10】 【11】 Bayes identification procedures Improved neural network algorithm MBP noise system identification procedure 【12】 multi-dimensional nonlinear function identification program Matlab program 【13】 fuzzy neural network decoupling Matlab program 【14】 F-test part of the program Platform: |
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Author:jshuska |
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Description: 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, ExampleModel.mdl, Friction_Model.mdl. Any other data files used in the presentation already ship with the toolbox (ver 7.0).
Products used:
- You basically need only System Identification Toolbox (SITB) to try out most examples.
- To use Simulink blocks, you would, of course, need Simulink.
- Control System Toolbox is used at one place to show how estimated models can be converted into LTI objects (SS, TF etc)
- Optimization Toolbox will be used if available for grey box estimation. If not, SITB s built-in optimizers will be used automatically.
- Other products mentioned: Neural Network Toolbox, Model Predictive Control Toolbox and Robust Control Toolbox.
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Author:陈翼男 |
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Description: 文章展示了一种新的方法用于功率系统中短期负载预测。提出的方案使用混沌时间序列分析基于确定性混沌去捕捉复杂的负载行为特征。确定性的混沌允许我们重构一个时间序列并决定输入的变量个数。这篇文章描述了混沌时间序列对日间功率系统峰值的分析。确定性混沌的非线性图形通过多层感知器的神经网络得到。提出的方案在一个例子中具体阐述。-This paper presents a new approach to short-term load forecasting in power systems. The proposed method makes use of chaos time series analysis that is based on deterministic chaos to capture characteristics of complicated load behavior. Deterministic chaos allows us to reconstruct a time series and determine the number of input variables. This paper describes chaos time series analysis of daily power system peak loads. The nonlinear
mapping of deterministic chaos is identified by the multilayer perceptron of an artificial neural network. The proposed approach is demonstrated in an example. Platform: |
Size: 382976 |
Author:will |
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Description: 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. Platform: |
Size: 14336 |
Author:罗波 |
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Description: 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 quality evaluation method of research is to be a modern automatic transmission vehicle technology development needs and proposed course of cutting-edge research
Questions, the evaluation process can be regarded as a nonlinear dynamic system. BP neural network nonlinear system identification, can approximate any
A nonlinear function. Assessed by determining the shift quality indicators, using data obtained from BP network training sample in order to establish
Subjective and objective evaluation criteria linkages using Mat lab/Simulink complete shift quality and objective description of evaluation methods
The simulation. Compared with the experimental results show that this method can effectively evaluate the true quality and the traditional subjective shift
Evaluation method has a good consistency.
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Author:杜晓帆 |
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Description: BP神经网络，包括由大量的简单基本元件——神经元相互联接而成的自适应非线性动态系统。每个神经元的结构和功能比较简单，但大量神经元组合产生的系统行为却非常复杂。-BP neural network，By a large number of simple basic components- neurons interconnected by adaptive nonlinear dynamic systems. The structure and function of each neuron is relatively simple, but a large number of neurons in combination to produce the system behavior is very complex. Platform: |
Size: 5120 |
Author:曾军 |
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Description: 在工程应用中经常会遇到一些复杂的非线性系统, 这些系统状态方程复杂, 难以用数学方法准确建模。在这种情况下, 可以建立B P神经网络表达这些非线性系统。该方法把未知系统看成是一个黑箱, 首先用系统输入输出数据训练B P神经网络, 使网络能够表达该未知函数, 然后就可以用训练好的B P神经网络预测系统输出。-In engineering applications often encounter a number of complex nonlinear systems, complex equation of state of these systems is difficult to accurately modeled using mathematical methods. In this case, BP neural network can be established express these nonlinear systems. The method of the unknown system as a black box, first with the system input and output data to train BP neural network so that the network can express the unknown function, then you can use the trained BP neural network prediction system output. Platform: |
Size: 52224 |
Author:吴江 |
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Description: 关于神经网络的案例和源代码
一共43个。
BP神经网络的数据分类，BP神经网络的非线性系统建模，遗传算法优化BP神经网络等等。-Cases and Source Code on Neural Networks.
A total of 43.
Data classification BP neural network, nonlinear system BP neural network modeling, genetic algorithm optimization BP neural network and so on. Platform: |
Size: 23837696 |
Author:言身寸 |
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Description: 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. Platform: |
Size: 1994752 |
Author:宋晨 |
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Search - neural network nonlinear system