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Description: 比较两个神经原网络的效率，不错的程序，有利于学习编程-compare two neuron network efficiency, good procedures and conducive to learning program
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Size: 1083 |
Author: 王汉卿 |
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Description: 基于RBF神经网络辨识的单神经元PID模型参考自适应控制-RBF neural network-based identification of single neuron PID MRAC
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Size: 1704 |
Author: 满延杰 |
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Description: 单神经元PID控制器的仿真与应用实例。比较常规PID控制器，得其优缺点-single neuron PID controller simulation and application examples. Comparing Conventional PID controller, to gain advantages
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Size: 1273 |
Author: 杜蘅 |
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Description: BP网络神经元控制器BPNNC-BPNNC of the BPnetwork neuron controller
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Size: 64888 |
Author: binjml |
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Description: LONWORKS NEURON C 浮点数头文件FLOAT.H
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Size: 1575 |
Author: xyz |
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Description: An optimal neuron evolution algorithm for the restoration
of linearly distorted images is presented in this paper. The proposed
algorithm is motivated by the symmetric positive-definite quadratic programming
structure inherent in restoration. Theoretical analysis and experimental
results show that the algorithm not only significantly increases
the convergence rate of processing, hut also produces good restoration
results. In addition, the algorithm provides a genuine parallel processing
structure which ensures computationally feasible spatial domain image
restoration
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Size: 8907582 |
Author: jindong |
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Description: 在LON网程序设计中使用Neuron C语言。Neuron C是一种基于ANSIC且带有网络通信和高级硬件设备接口扩展语句的高级语言。它增加了对I/O、事件处理、消息传递和分散数据目标的支持， 扩充了包括软定时器、网络变量、显示消息、一个多任务调度程序以及其它各具特点的函数等。采用Neuron C语言开发的应用程序，可直接在Lonbuilder神经元仿真器上进行调试，因此应用程序的开发可独立于硬件设计进行
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Size: 3680 |
Author: zgc |
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Description: Simulation describing the electrophysiological behavior of a biological neuron (nerve cell). Two sets of membrane kinetics are included: Hodgkin-Huxley and Schwarz for unmyelinated and myelinated axons.
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Size: 1510320 |
Author: ROGER |
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Description: Hopfield 网——擅长于联想记忆与解迷路 实现H网联想记忆的关键，是使被记忆的模式样本对应网络能量函数的极小值。 设有M个N维记忆模式，通过对网络N个神经元之间连接权 wij 和N个输出阈值θj的设计，使得: 这M个记忆模式所对应的网络状态正好是网络能量函数的M个极小值。 比较困难，目前还没有一个适应任意形式的记忆模式的有效、通用的设计方法。 H网的算法 1）学习模式——决定权重 想要记忆的模式，用-1和1的2值表示 模式：-1，-1，1，-1，1，1，... 一般表示： 则任意两个神经元j、i间的权重： wij=∑ap(i)ap(j)，p=1…p； P：模式的总数 ap(s)：第p个模式的第s个要素（-1或1） wij：第j个神经元与第i个神经元间的权重 i = j时，wij=0，即各神经元的输出不直接返回自身。 2)想起模式： 神经元输出值的初始化 想起时，一般是未知的输入。设xi(0)为未知模式的第i个要素（-1或1） 将xi(0)作为相对应的神经元的初始值，其中，0意味t=0。 反复部分：对各神经元，计算： xi (t+1) = f (∑wijxj（t）-θi), j=1…n, j≠i n—神经元总数 f（）--Sgn（） θi—神经元i发火阈值 反复进行，直到各个神经元的输出不再变化。-Hopfield network -- good at associative memory solution with the realization of lost H associative memory networks, are key to bringing the memory model samples corresponding network energy function of the minimum. With M-N-dimensional memory model, the network N neurons connect between right wij and N output threshold j design makes : M-mode memory corresponding to the network is a state network energy function is the M-000 minimum. More difficult, it is not an arbitrary form of adaptation memory model of effective, common design methods. H network algorithm 1) mode of learning -- decision weights want memory model, with 1 and 2 of the value of a model, said : -1, 1, 1, 1 ,1,1, ... in general : two were arbitrary neuron j i weights between : wij ap = (i) ap (j), p = 1 ... p; P : The tot
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Size: 11421 |
Author: 韵子 |
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Description: The program Neuron.c simulates a SIMPLE stable state neural networkreporting on both input and output states and energy levels after eachiteration (namely set up for 8, though usually the network stabilizes afterabout 4). The program demonstrates a very straight-forward method ofprogramming a content-addressable memory and receiving output from that memory.
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Size: 4365 |
Author: 王斌 |
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Description: /*神经元模型*/ 学习 重新学习 实践新模型 演示已有模型-/ * neuron model * / re-study and practice to learn the new model has been demonstrated Model
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Size: 1730 |
Author: 王想 |
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Description: 单神经元网络PID自动整定MATLAB程序-single neuron network automatic PID tuning MATLAB
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Size: 1272 |
Author: 张俊 |
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Description: 用BP网络完成函数的逼近 P网络通常有一个或多个隐层，隐层中的神经元均采用sigmoid型变换函数，输出层的神经元采用纯线性变换函数。本例应用一个两层BP网络来完成函数逼近的任务，其中隐层的神经元个数是5。-network to be completed by BP function approximation P networks usually have one or more hidden layer, a hidden layer neurons are used sigmoid-type transformation function, the output layer neurons using pure linear transformation function. The application of a two-tier cases BP network function approximation to complete the task, hidden layer neuron number is 5.
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Size: 1247 |
Author: 王得 |
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Description: Hopfield 网——擅长于联想记忆与解迷路 实现H网联想记忆的关键，是使被记忆的模式样本对应网络能量函数的极小值。 设有M个N维记忆模式，通过对网络N个神经元之间连接权 wij 和N个输出阈值θj的设计，使得: 这M个记忆模式所对应的网络状态正好是网络能量函数的M个极小值。 比较困难，目前还没有一个适应任意形式的记忆模式的有效、通用的设计方法。 H网的算法 1）学习模式——决定权重 想要记忆的模式，用-1和1的2值表示 模式：-1，-1，1，-1，1，1，... 一般表示： 则任意两个神经元j、i间的权重： wij=∑ap(i)ap(j)，p=1…p； P：模式的总数 ap(s)：第p个模式的第s个要素（-1或1） wij：第j个神经元与第i个神经元间的权重 i = j时，wij=0，即各神经元的输出不直接返回自身。 2)想起模式： 神经元输出值的初始化 想起时，一般是未知的输入。设xi(0)为未知模式的第i个要素（-1或1） 将xi(0)作为相对应的神经元的初始值，其中，0意味t=0。 反复部分：对各神经元，计算： xi (t+1) = f (∑wijxj（t）-θi), j=1…n, j≠i n—神经元总数 f（）--Sgn（） θi—神经元i发火阈值 反复进行，直到各个神经元的输出不再变化。-Hopfield network-- good at associative memory solution with the realization of lost H associative memory networks, are key to bringing the memory model samples corresponding network energy function of the minimum. With M-N-dimensional memory model, the network N neurons connect between right wij and N output threshold j design makes : M-mode memory corresponding to the network is a state network energy function is the M-000 minimum. More difficult, it is not an arbitrary form of adaptation memory model of effective, common design methods. H network algorithm 1) mode of learning-- decision weights want memory model, with 1 and 2 of the value of a model, said :-1, 1, 1, 1 ,1,1, ... in general : two were arbitrary neuron j i weights between : wij ap = (i) ap (j), p = 1 ... p; P : The tot
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Size: 11264 |
Author: 韵子 |
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Description: 单神经元网络PID自动整定MATLAB程序-single neuron network automatic PID tuning MATLAB
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Size: 1024 |
Author: 张俊 |
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Description: 用BP网络完成函数的逼近 P网络通常有一个或多个隐层，隐层中的神经元均采用sigmoid型变换函数，输出层的神经元采用纯线性变换函数。本例应用一个两层BP网络来完成函数逼近的任务，其中隐层的神经元个数是5。-network to be completed by BP function approximation P networks usually have one or more hidden layer, a hidden layer neurons are used sigmoid-type transformation function, the output layer neurons using pure linear transformation function. The application of a two-tier cases BP network function approximation to complete the task, hidden layer neuron number is 5.
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Size: 1024 |
Author: 王得 |
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Description: LVQ学习矢量化算法源程序
This directory contains code implementing the Learning vector quantization
network. Source code may be found in LVQ.CPP. Sample training data is found
in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The
LVQ program accepts input consisting of vectors and calculates the LVQ
network weights. If a test set is specified, the winning neuron (class) for
each neuron is identified and the Euclidean distance between the pattern and
each neuron is reported. Output is directed to the screen.-LVQ learning vector algorithm This directory contains source co de implementing the Learning vector quantizat ion network. Source code may be found in LVQ.CPP . Sample training data is found in LVQ1.PAT. Sam ple test data is found in LVQTEST1.TST and LVQTE ST2.TST. The program accepts input LVQ consist ing of vectors and calculates the network we LVQ ights. If a test set is specified, the winning Neurology (class) for each of Neurology is id entified and the Euclidean distance between th e pattern and each of Neurology is reported. Output is directed to the screen.
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Size: 37888 |
Author: 张伟华 |
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Description: 自适应PSD神经元控制,用于伺服电机的调速度,比单神经元有跟好的鲁帮性-adaptive neural control for the servo-motor speed, a single neuron with good Lu hand
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Size: 1024 |
Author: wang |
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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: 3072 |
Author: 陈光华 |
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Description: 单输出的神经元网络拟合有噪声干扰的函数：y=x1/(1+x1) + x2/(1+x2) + x3/(1+x3) + x4/(1+x4)-single output neuron network fitting a noise function : y = x/(1 x1) x2/(1 x2) x3/(1 x3) x/(1 x4)
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Size: 6144 |
Author: 周善人 |
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