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Description: 这是一个基于模拟退火算法的混沌神经网络模型-This is an algorithm based on simulated annealing chaotic neural network model
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Size: 1320 |
Author: 袁颂岳 |
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Description: 这个是经典Aihara混沌神经网络模型的程序-This is a classic Aihara chaotic neural network model procedures
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Size: 1028 |
Author: 袁颂岳 |
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Description: 这是一个基于模拟退火算法的混沌神经网络模型-This is an algorithm based on simulated annealing chaotic neural network model
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Size: 1024 |
Author: 袁颂岳 |
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Description: 这个是经典Aihara混沌神经网络模型的程序-This is a classic Aihara chaotic neural network model procedures
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Size: 1024 |
Author: 袁颂岳 |
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Description: 这是一个混沌理论中重要的细胞神经网络的程序。通过曲线很容易看到奇妙的超混沌吸引子-Chaos Theory important cellular neural network procedures. Curve it is easy to see the mysterious super-chaotic attractor
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Size: 1024 |
Author: liuwei |
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Description: 应用遗传算法与类神经网络于混沌系统之辨识.pdf-application of genetic algorithms and neural networks in the chaotic system of identification. Pdf
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Size: 303104 |
Author: 肖晨光 |
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Description: 混沌时间序列预测工具箱，包括了李雅普诺夫指数、分形纬、嵌入纬以及神经网络预测-chaotic time series forecasting tool kit, including the Lyapunov exponent, fractal-wai, Wei and embedded neural network prediction
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Size: 355328 |
Author: 四度 |
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Description: 混沌神经网络的Lyapumov指数与混沌区域-Chaotic Neural Network and Chaos Lyapumov index of regional
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Size: 300032 |
Author: xiaowang |
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Description: 资源分配神经网络解决Mackey-Glass时间序列预测函数逼近问题-Neural network to solve the allocation of resources Mackey-Glass time series prediction function approximation problem
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Size: 1024 |
Author: 吴强 |
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Description: 两本神经网络方面的经典电子书
人工神经网络导论.pdf
人工神经网络实用教程.pdf
神经网络是智能控制技术的主要分支之一。本书的主要内容有：神经网络的概念，神经网络的分类与学习方法，前向神经网络模型及其算法，改进的BP网络及其控制、辨识建模，基于遗传算法的神经网络，基于模糊理论的神经网络，RBF网络及其在混沌背景下对微弱信号的测量与控制，反馈网络，Hopfield网络及其在字符识别中的应用，支持向量机及其故障诊断，小波神经网络及其在控制与辨识中的应用。-Two neural networks classic book Introduction to Artificial Neural Networks. Pdf Practical Guide artificial neural network. Pdf neural network is the intelligent control technology, one of the main branch. The main contents of this book are: the concept of neural networks, neural network classification and learning methods, the former to the neural network model and its algorithm, the improved BP network and its control, recognition modeling, based on genetic algorithm neural network, based on fuzzy the theory of neural network, RBF network and its application in the context of chaotic signals of weak measurement and control, feedback network, Hopfield Network and Its Application in character recognition, support vector machine and its fault diagnosis, wavelet neural network and its application in control and identification applications.
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Size: 7793664 |
Author: 梁健 |
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Description: 通过细胞神经网络混沌系统对图像数据进行加密和解密 -Through the cellular neural networks for image data of chaotic systems for encryption and decryption
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Size: 94208 |
Author: daijiaxing |
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Description: 混沌神经网络模型，包括二个和三个混沌神经元组成的网络-Chaotic neural network model, including two and three chaotic neuron network
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Size: 2048 |
Author: 廖洪运 |
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Description: 为了选择神经网络的最好结构以及增强模型的推广能力，提出一种自适应支持向量回归神经网络(SVR—NN)。SVR—NN 用支持向量回归(SVR)方法获得网络的初始结构和权值， 白适应地生
成网络隐层结点，然后用基于退火过程的鲁棒学习算法更新网络结点疹教和权 主。 SVR—NN有很
好的收敛性和鲁棒性，能抑制由于数据异常和参数选择不当所导致的“过拟合，’现象。将SVR—NN
应用到时间序列预测上。结果表明，SVR．NN预测模型能精确地预测混沌时间序列，具有很好的
理论和应用价值。-Abstract：To select the‘best’structure of the neural networks and enhance the generalization ability of models．a support
vector regression neural networks fSVR-NN)was proposed．Firstly，support vector regression approach was applied to
determine initial structure and initial weights of SVR．NN SO that the number of hidden layer nodes can be constructed
adaptively based on support vectors．Furthermore，an annealing robust learning algorithm was further presented to fine
tune the hidden node parameters and weights of SVR一ⅣM The adaptive SVR．NN has faSt convergence speed and robust
capability．and it can also suppress the ‘orerfitting’phenomena when the train data ncludes outliers．The adaptive
SVR．NN was then applied to time series prediction．Experimental results show that the adaptive SVR．ⅣⅣ can accurately
predict chaotic time series，and it iS valuable in both theory and application aspects．
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Size: 316416 |
Author: 11 |
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Description: 用四阶经典龙格库塔算法求解5阶细胞神经网络状态方程，得到5路超混沌序列。-With the fourth order classical Runge-Kutta algorithm for 5-order cellular neural network state equation, 5-way super-chaotic sequence are generated.
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Size: 1024 |
Author: 赵志广 |
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Description: 这是一个混沌神经网络的matlab程序，可用于实现基于混沌神经网络的预测和建模。-This is a chaotic neural network matlab program for chaotic neural network-based forecasting and modeling
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Size: 1024 |
Author: 申冲 |
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Description: 混沌时间序列 基于人工神经网络的一步或多步预测-Chaotic time series based on artificial neural network or multi-step prediction step
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Size: 5120 |
Author: wuyong |
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Description: 改进混沌神经网络的舵面故障预测Improved chaotic neural network prediction of control surface failure-Improved chaotic neural network prediction of control surface failure
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Size: 202752 |
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Description: 本程序画有时滞的混沌神经网络,本程序简单而且运算速度很快.-This code is to painting time delayed chaotic neural networks, moreover, the code is simple and operating quick.
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Size: 40960 |
Author: yangxinsong |
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Description: 本程序画无时滞的混沌神经网络,本程序简单而且运算速度很快.-The painting process without time-delay chaotic neural network, this process simple and fast operation.
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Size: 39936 |
Author: yangxinsong |
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Description: Multi-step-prediction of chaotic time series based
on co-evolutionary recurrent neural network
协同进化递归神经网络的多步混沌时间序列预测-This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic
time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent
neural networks by co-evolutionary strategy. The searching space was separated into two subspaces and the individuals
are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability
of recurrent neural network to incorporate past experience due to internal recurrence. The eff ectiveness of CERNN is
evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey–Glass series and real-world
sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic
time series.
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Size: 152576 |
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