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[AI-NN-PR] hopfild1 DL : 0: 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
Date : 2025-12-24 Size : 11kb User : 韵子
[AI-NN-PR] GAfunction DL : 0: 请大家看一看, 我编的这个用遗传算法求 f(x)=xsin(10pi*x)+2.0 x为-1到2区间的值-Please look, I spent part of the genetic algorithm for f (x) = xsin (10pi* x) 2.0 x 2-1 range of values
Date : 2025-12-24 Size : 2kb User : 徐春鸽
[AI-NN-PR] GeneticAlgorithms_matlab DL : 0: X(t)=Asin(2*pi *f *t+ q)+n(t) 估计其中的参数为A，f， q。n(t)为随机噪声，服从正态分布。其他的具体见附件中的程序 -X (t) = 4sin (2* pi* f* t q) n (t) is estimated parameters A, f, q. N (t) of random noise, subject to normal. Other specific see annex to the proceedings
Date : 2025-12-24 Size : 6kb User : 戴朝华
[AI-NN-PR] Netlabtoolbox DL : 0: The Netlab toolbox is designed to provide the central tools necessary for the simulation of theoretically well founded neural network algorithms and related models for use in teaching, research and applications development. It contains many techniques which are not yet available in standard neural network simulation packages-The toolbox is designed to provide t he central tools necessary for the simulation o f theoretically well founded neural network al gorithms and related models for use in teaching , research and applications development. It c ontains many techniques which are not yet avail able in standard neural network simulation pac kages
Date : 2025-12-24 Size : 248kb User : lwd
[AI-NN-PR] Genetic_algorithm01 DL : 0: 遗传算法源程序，求解一个简单优化问题f(x)=x1^2+x2^2,-5<=x1<=5,-5<=x2<=5-genetic algorithm source files, for a simple optimization problem f (x) = x ^ 2 x ^ 2,-5
Date : 2025-12-24 Size : 2kb User : 宋仁栋
[AI-NN-PR] rjMCMCsa DL : 0: 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.
Date : 2025-12-24 Size : 16kb User : 徐剑
[AI-NN-PR] IA DL : 0: 一个基于Matlab的人工免疫算法％Immune Algorithm based on the immune network model for function f(x1,x2) optimum-Matlab-based artificial immune algorithm Immune Algorithm based on the immune network model for function f (x1, x2) optimum
Date : 2025-12-24 Size : 5kb User : tangkezong
[AI-NN-PR] GA DL : 0: 介绍了遗传算法的基本原理和求解流程, 详细阐述了Matlab 遗传算法工具箱的使用方法, 并通过使用遗传算法工具箱对一个典型的函数优化问题进行求解, 验证了该工具箱在解决函数优化问题上的有效性和实用性。-Introduce a genetic algorithm to solve the basic principle and flow in detail Matlab genetic algorithm toolbox to use, and by using genetic algorithm toolbox for a typical function optimization problems to solve, the validity of the toolbox in solving function optimization problems on the effectiveness and practicality.
Date : 2025-12-24 Size : 50kb User : 崔艳
[AI-NN-PR] RBF DL : 0: 用RBF神经网络，完成对y=f(x)的曲线拟合。-Using RBF neural networks, complete the y = f (x) the curve fitting.
Date : 2025-12-24 Size : 21kb User : 徐波
[AI-NN-PR] klh DL : 0: 阐述了人工免疫系统的基本概念"讨论了几种典型的算法"包括基于免疫系统基本机制的免疫算法$基于免疫特异性的否定选择算法-&--F)@+2G*F*E3 J)F*F*4J3 F,/+I+2G KF@4/5+00123 545*30!&’(.&)*+,+-+./’00123(45*30$+5 J)FJF53@$L.53@ F2*E3 0.+2,12-*+F2 F,+00123 545*30!*E3*4J+-././GF)+*E05.)3@35-)+K3@!51-E.5+00123./GF)+*E0!23G.*+I3.2@ -/F2./53/3-*+F2./GF)+*E05!+00123 3IF/1*+F2.)4./GF)+*E0!&’(<231)./23*MF)=0+>+2*3//+G32*545*30!,1NN4+00123 545O *30.2@5F F2$
Date : 2025-12-24 Size : 2kb User : 小蓝
[AI-NN-PR] edrk DL : 0: 主要包括免疫识别、免疫学习、免疫记忆、克隆选择、个体多样性、分布式和自适应等,-It is the real engineering app licat ion s that draw the b road at ten2 t ion of compu ter scien t ist s to recogn ize the great po ten t ial of A IS, hereby som e impo rtan t app li2 cat ion f ields as info rm at ion secu rity, pat tern recogn it ion, op t im izat ion, m ach ine learn ing, data m in ing, robo t ics, diagno st ics and cybernet ics etc. are review ed
Date : 2025-12-24 Size : 1kb User : 小蓝
[AI-NN-PR] mRMRFeatureSelection DL : 0: mRMR_0.9_compiled最小冗余和最大相关特征选取源代码，-This package is the mRMR (minimum-redundancy maximum-relevancy) feature selection method, whose better performance over the conventional top-ranking method has been demonstrated on a number of data sets in recent publications. This version uses mutual information as a proxy for computing relevance and redundancy among variables (features). Other variations such as using correlation or F-test or distances can be easily implemented within this framework, too.
Date : 2025-12-24 Size : 997kb User : 韩华
[AI-NN-PR] gabp-src DL : 0: a matlab code for training a backpropagation neural network
Date : 2025-12-24 Size : 251kb User : ssss
[AI-NN-PR] mnth DL : 0: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。 -Simulated annealing algorithm derived from the theory of solid annealing, the solid heat to full high and let it slowly cooling, heating, the temperature rise inside the solid particles with the shape into disorder, which can be increased gradually while slowly cooling particles increasingly ordered, the temperature has reached equilibrium in each state, and finally reached the ground state at room temperature, which can be reduced to minimum. According to Metropolis criterion, particles tend to equilibrium at a temperature T, the probability e-ΔE/(kT), where E is the temperature T, internal energy, ΔE change its volume, k the Boltzmann constant. Simulated annealing with a solid portfolio optimization problem, the internal energy E is modeled as the objective function value f, temperature T evolved into control parameter t, which are solutions of combinatorial optimization problems of the simulated annealing algorithm: the initial solution from the initial value of t i and the control
Date : 2025-12-24 Size : 5kb User : leansmall
[AI-NN-PR] BP_zuoye DL : 0: 基于MATLAB的神经网络控制，用BP神经网络拟合函数f = exp(-1.9*(u+ 0.5))*sin(10*u)-Neural network control based on MATLAB, using BP neural network fitting function f = exp (-1.9* (u+ 0.5))* sin (10* u)
Date : 2025-12-24 Size : 1kb User : 张刚
[AI-NN-PR] Adaptive-Embedding-Dimension DL : 0: 嵌入维数自适应最小二乘支持向量机状态时间序列预测方法 Condition Time Series Prediction Using Least Squares Support Vector Machine with Adaptive Embedding Dimension 针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题, 提出一种基于嵌入维数自适应最小二乘支持向量机( L SSVM ) 的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重要参数, 以交叉验证误差为评价准则, 利用粒子群优化( P SO ) 进化搜索LSSV M 预测模型的最优超参数与嵌入维数, 同时通过矩阵变换原理提高交叉验证过程的计算效率, 并最终建立优化后的L SSVM 预测模型。航空发动机排气温度( EGT ) 预测实例表明, 该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测精度高, 适用于航空发动机状态时间序列预测。- T o deal wit h the difficulty of selecting an appro pr iate embedding dimension for aeroeng ine co ndition time series predictio n, a metho d based o n least squar es suppo rt vecto r machine ( L SSVM ) with ada ptive em bedding dimension is pro po sed. I n the method, the embedding dimensio n is identified as a parameter that af fects the accuracy o f the aer oengine condition time series predictio n par ticle sw arm o ptimizat ion ( P SO) is ap plied to optimize the hyperpar ameter s and embedding dimension of the L SSV M pr edict ion model cro ssv alida tion is applied to evaluate the perfo rmance o f the L SSVM predictio n mo del and matr ix tr ansfo rm is applied to the L SSVM pr ediction model tr aining to accelerate the crossvalidation evaluation pro cess. Ex periments on an aeroengine ex haust g as t emperatur e ( EGT ) predictio n demonst rates that the metho d is hig hly effective in em bedding dimension selection. In compar ison w ith co nv
Date : 2025-12-24 Size : 334kb User :
[AI-NN-PR] T-F DL : 0: 优化算法经典测试函数MATLAB源码，包括ROSENBROCK等，可与优化算法配合使用-Some beachmark functions about optimization, can use with some algorithm like PSO
Date : 2025-12-24 Size : 8kb User : 刘角
[AI-NN-PR] ES_Lin DL : 0: 进化策略算法matlab代码，实现求f(x) = x*sin(10*pi*x) + 1最大值的功能。-Evolutionary strategy algorithm matlab code to achieve demand f (x) = x* sin (10* pi* x)+ 1 maximum functionality.
Date : 2025-12-24 Size : 1kb User : 许根鹏