Description: 用粒子群算法来优化RBF神经网络权值，使神经网络有更好的非线性函数逼近能力-Using particle swarm optimization to optimize the RBF neural network weights, so that neural network has better ability of nonlinear function approximation Platform: |
Size: 3072 |
Author:史峰 |
Hits:
Description: 基于BP神经网络识别字符.
BP神经网络算法是把一组样本输入输出问题转化为一个非线性优化问题，并通过梯度算法利用迭代运算求解权值的一种学习方法。采用BP网络进行分类，并附加线性感知器来实现单字符的有效识别，算法简便，识别率高，可适用于多种高噪声环境中的印刷体字符识别。-BP neural network based character recognition. BP neural network algorithm is a set of sample input and output is transformed into a nonlinear optimization problem, and through the use of iterative gradient algorithm for computing the value of a solution of the right way of learning. BP network used for classification, and additional linear perceptron to achieve an effective single-character recognition, the algorithm is simple, a high recognition rate, applicable to a wide range of high-noise environments print character recognition. Platform: |
Size: 113664 |
Author:吕寿鹏 |
Hits:
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.
Platform: |
Size: 34816 |
Author:陈翼男 |
Hits:
Description: 算法大全 全书分30章及2附录（在MATLAB中实现）对常用数学算法进行汇总介绍。
主要包括：线性规划、非线性规划、动态规划、图与网络、排队论、对策论、层次分析法、插值与拟合、数据的统计描述和分析、方差分析、回归分析、微分方程建模、稳定状态模型、常微分方程解法、差分方程模型、马氏链模型、变分法模型、神经网络模型、偏微分方程的数值解、目标规划、模糊数值模型、现代优化算法、时间序列模型、存贮论、经济与金融的优化问题、生产与服务运作管理中的优化问题、灰色系统理论及其应用、多元分析、偏最小二乘回归以及附录-Complete collection of algorithm including 30 chapters and 2 appendices: linear programming, nonlinear programming, dynamic programming, graph and networks, queuing theory, game theory, the level of analysis, interpolation and fitting, statistical description and analysis of data , analysis of variance, regression analysis, differential equations modeling, steady-state model, ordinary differential equation solution, difference equation model, Markov chain model, variational method model, neural network model, the numerical solution of partial differential equations, goal programming, fuzzy numerical model, modern optimization algorithms, time series models, storage theory, the optimization of economic and financial issues, production and service operations management in the optimization problem, gray system theory and its applications, multivariate analysis, partial least squares regression, and Appendix
Platform: |
Size: 7684096 |
Author:商志远 |
Hits:
Description: 神经网络训练拟合根据寻优函数的特点构建合适的BP神经网络,用非线性函数的输入输出数据训练BP神经网络,训练后的BP神经网络就可以预测函数输出。遗传算法极值寻优
把训练后的BP神经网络预测结果作为个体适应度值,通过选择、交叉和变异操作寻找函数的
全局最优值及对应输入值。
-Neural network training function fitting based optimization features built right on BP neural network, using non-linear function of the input output data trained BP neural network, the trained BP neural network can predict the function output. The genetic algorithm optimization extreme training BP neural network prediction results as the individual fitness value through selection, crossover and mutation find the global optimal value function and the corresponding input value. Platform: |
Size: 3072 |
Author:吴军 |
Hits:
Description: 本课题首先根据寻优函数的特点构建合适的BP神经网络,用非线性函数的输入输出数据训练BP神经网络,训练后的BP神经网络就可以预测函数输出。遗传算法极值寻优
把训练后的BP神经网络预测结果作为个体适应度值,通过选择、交叉和变异操作寻找函数的
全局最优值及对应输入值。
-Neural network training function fitting based optimization features built right on BP neural network, using non-linear function of the input output data trained BP neural network, the trained BP neural network can predict the function output. The genetic algorithm optimization extreme training BP neural network prediction results as the individual fitness value through selection, crossover and mutation find the global optimal value function and the corresponding input value. Platform: |
Size: 4096 |
Author:吴军 |
Hits:
Description: 本文介绍在Matlab 环境下发电厂水位控制BP 模型的建模与仿真方法, 得出了预测模型的最优参数。仿真结果表明, MATLAB
神经网络工具箱可有效地用来解决复杂的非线性控制系统的优化设计问题。-This article describes the Matlab environment plant level control BP Model modeling and simulation methods, predictive models obtained optimal parameters. Simulation results show that, MATLAB
Neural Network Toolbox can be effectively used to solve complex nonlinear control system optimization design problems. Platform: |
Size: 802816 |
Author:cup |
Hits:
Description: 程序1：遗传算法和非线性规划函数的优化；
程序2：基于遗传算法的BP神经网络优化；
程序3：基于遗传算法的TSP算法；
程序4：基于遗传算法的LQR控制器优化设计
程序5：基于遗传算法的函数优化-Program 1: genetic algorithms and nonlinear programming function optimization Program 2: Based on the genetic algorithm BP neural network optimization Program 3: TSP algorithm based on genetic algorithm Program 4: LQR controller based on genetic algorithm optimization procedures 5: Function Optimization Based on Genetic Algorithm Platform: |
Size: 5120 |
Author:王晓卫 |
Hits:
Description: MATLAB神经网络30个案例分析，遗传算法优化BP神经网络-非线性函数拟合等30种代码-MATLAB neural network 30 case studies, genetic algorithm optimization BP neural network- a nonlinear function fitting and other 30 kinds of code Platform: |
Size: 7137280 |
Author:李想 |
Hits:
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 |
Hits:
Description: 对于未知的非线性函数，仅通过函数的输入输出数据难以准确寻找函数极值，这类问题可以通过神经网络结合遗传算法求解，利用神经网络的非线性拟合能力和遗传算法的非线性寻优能力寻找函数极值。-For unknown nonlinear function, only through the function of input and output data is difficult to accurately find the function extreme value, this kind of problem can be through the neural network combined with genetic algorithm, using the nonlinear fitting ability of the neural network and nonlinear optimization ability of genetic algorithm to find the function extreme value. Platform: |
Size: 303104 |
Author:汪杰 |
Hits:
Description: MATLAB最优化计算神经网络优化，预测，滤波算法，针对非线性信号的拟合，求出信号相关参数和计算结果-Neural network optimization, prediction, filtering algorithm, for the nonlinear signal fitting, obtained signal related parameters and calculation results Platform: |
Size: 160768 |
Author:双月 |
Hits:
Description: BP神经网络容易陷于局部极小值，PSO算法在无约束非线性函数优化方面性能优越，通常可以直接找寻到全局最优解，即使不能搜多到全局最优解，也距离全局最优点不远。当然，基本PSO算法陷入局部极值也是有的。对于这个缺点目前还没有找到比较有效、省市的解决方案。本案例实现利用PSO算法和BP算法共同训练神经网络，先将网络进行PSO算法训练，然后BP算法接着进行小范围精细搜索，PSO算法训练神经网络的本质就是将输出误差函数（即能量函数）看成目标函数，PSO对能量函数进行全局寻找最小值。(BP neural networks are prone to local minimum values. The PS algorithm has superior performance in the optimization of unconstrained nonlinear functions. It can usually find the global optimal solution directly. Even if it can not find more global optimal solutions, it is not far from the global best. Of course, there is also a local extreme of the basic PO algorithm. For this shortcoming, there is no more effective, provincial and municipal solution. This case realizes the use of the SO algorithm and the BP algorithm to train the neural network. First, the network is trained in the SO algorithm, and then the BP algorithm is followed by a small range of fine searches. The essence of the PO algorithm training neural network is to regard the output error function(ie, the energy function) as the objective function, and the PO seeks a global minimum value for the energy function.) Platform: |
Size: 3072 |
Author:Katri |
Hits:
Search - nonlinear optimization neural network optimization