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Description: 开发环境:MATLAB
简要说明:基于MATLAB的显示误差递变的线性神经元源程序-development environment : MATLAB Brief Description : Based on MATLAB error shows the linear movements of neurons source
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Size: 902 |
Author: 刘华 |
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Description: 用神经元模拟磁旋，用连接权模拟磁场中磁旋的相互作用，用各神经元的激活、抑制这两种状态，模拟磁旋的上下两个方向。
构成一个具有记忆功能的NN，并用计算能量函数，来评价和指导整个网络的记忆功能。
-using simulated neurons magnetic spins, and connecting with the right analog magnetic rotary magnetic field interaction with the activation of neurons, contain two states, analog magnetic rotary direction of the next two. Constitute a memory function of NN, and the computational energy function, and guidance to evaluate the entire network of memory function.
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Size: 64480 |
Author: fuyu |
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Description: 该程序模拟了神经元学习的过程.在学习过程中通过改变权值来慢慢接近教师信号.-the program to simulate the neurons learning process. In the process of learning by changing the value of the right of teachers to slowly close signal.
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Size: 1997 |
Author: 飘雨 |
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Description: 一个在unix下运行的neurons EA小程序-a run in the neurons EA small program
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Size: 2229 |
Author: 李华康 |
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Description: BP算法最新C源程序,
#include\"stdarg.h\"
#include\"stdio.h\"
#include\"stdlib.h\"
#include\"math.h\"
#include\"graphics.h\"
#include\"conio.h\"
#define IN 4 /*输入层的神经元个数*/
#define HID 13 /*隐含层的神经元个数*/
#define MOD 594 /*学习样本个数*/
#define OUT 1 /*输出层的神经元个数*/-BP algorithm latest C source, # include "stdarg.h" # include "stdio.h" # include "stdlib.h" # include "math.h" # include "graphics.h" # include "conio.h" # define IN 4 / * input layer neurons number * / # define HID 13 / * hidden layer neuron number * / # define MOD 594 / * Number of samples * / # define OUT 1 / * output layer neurons number * /
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Size: 2958 |
Author: 陈光华 |
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Description: 对原有madaline算法改进，适应小范围变化对神经元带来的影响-the original neural algorithm to improve and adapt to small changes in the scope of the neurons affected
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Size: 437284 |
Author: WW |
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Description: 基于神经元算法的图像处理，效果挺不错的，大家可以-neurons algorithm based on the image processing, the effect sounds very good, we can learn
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Size: 58995 |
Author: 梁锋 |
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Description: 这是神经元模型的C语言代码。神经元模型在控制等有着极为重要的应用。-This is the neuron model C language code. Neurons in the control model has a very important application.
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Size: 1990 |
Author: 李天鸿 |
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Description: 一个在unix下运行的neurons EA小程序-a run in the neurons EA small program
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Size: 2048 |
Author: 李华康 |
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Description: On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 16384 |
Author: 徐剑 |
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Description: 神经网络中的神经元代码，用c++变的，变得不好，请大家指点-Neural network of neurons code, with c++ Change and become good, please instruct the U.S.
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Size: 2356224 |
Author: qinxuri |
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Description: This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.-This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 220160 |
Author: 晨间 |
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Description: This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 348160 |
Author: 晨间 |
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Description: 用小波构造神经网络，将小波分解的基函数构成神经的隐函数。-Using Wavelet neural networks, wavelet decomposition will constitute the basis function of the implicit function neurons.
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Size: 1024 |
Author: 杨丽 |
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Description: 利用一个单隐层BP网络来逼近一个函数，在改程序中有21组数据。该网络的输入层和输出层的神经元个数均为一。-Using a single hidden layer BP network to approximate a function, in the reform process there were 21 sets of data. The network input layer and output layer are a number of neurons.
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Size: 3072 |
Author: 白雪静 |
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Description: HH模型 神经元放电 龙格库塔算法 神经元激发行为-HH model neurons Runge-Kutta algorithm
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Size: 3072 |
Author: yangyang |
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Description: 人工神经网络就是模拟人思维的第二种方式。这是一个非线性动力学系统，其特色在于信息的分布式存储和并行协同处理。虽然单个神经元的结构极其简单，功能有限，但大量神经元构成的网络系统所能实现的行为却是极其丰富多彩的。
-Simulation of artificial neural network is a second way of human thinking. This is a nonlinear dynamic system, which features a distributed information storage and parallel co-processing. Although the structure of single neurons is extremely simple, limited functionality, but a large number of neurons in a network system can realize the behavior is very colorful.
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Size: 5120 |
Author: prince |
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Description: 在MATLAB中用M文件编写来实现四种不同的单神经元控制器-realize single-neurons PID Controller in MATLAB
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Size: 1024 |
Author: 琳儿 |
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Description: 对于两个不同神经元进行耦合后的体系进行模拟-Simulated for two different neurons coupled system
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Size: 3072 |
Author: 木棉花vc |
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Description: this program Calculates the optimum number of neurons for identifying steam by LOLIMOT.
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Size: 3072 |
Author: hamed |
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