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[AI-NN-PR] megahal-9.1.1 DL : 0: megahal is the conversation simulators conversing with a user in natural language. The program will exploit the fact that human beings tend to read much more meaning into what is said than is actually there MegaHAL differs from conversation simulators such as ELIZA in that it uses a Markov Model to learn how to hold a conversation. It is possible to teach MegaHAL to talk about new topics, and in different languages.-megahal conversation is the co simulators nversing with a user in natural language. The pr ogram will exploit the fact that human beings te nd to read much more meaning into what is said tha n is actually there MegaHAL differs from conver sation simulators such as ELIZA in that it uses a Markov Model to learn how to hold a conversation . It is possible to teach MegaHAL to talk about ne w topics, and in different languages.
Date : 2025-12-29 Size : 110kb User : txcomp
[AI-NN-PR] Substituter.java DL : 0: 代入法的启发示搜索我的代码实现是：按照自然语言各字母出现频率的大小从高到低（已经有人作国统计分析了）先生成一张字母出现频率统计表（A）--------(e),(t,a,o,i,n,s,h,r),(d,l),(c,u,m,w,f,g,y,p,b),(v,k,j,x,q,z) ，再对密文字母计算频率，并按频率从高到低生成一张输入密文字母的统计表（B），通过两张表的对应关系，不断用A中的字母去替换B中的字母，搜索不成功时就回退，在这里回朔是一个关键。 -generation into a search of inspiration I said a code is : According to the Natural Language alphabet frequency of the size of precedence (has been for the State Statistical Analysis), Mr. into an alphabet frequency tables (A )--------( e), (t, a, o, i, n, s, h r), (d, l), (c, u, m, w, f, g, y, p, b), (v, k, j, x, q, z), again close to the mother language calculated frequency and frequency input precedence generate a secret letter to the mother TAB (B), Table 2 by the corresponding relations, use of the letters A to B to replace the letters of the alphabet, when unsuccessful search regression, Here is a retrospective key.
Date : 2025-12-29 Size : 4kb User : rtshen
[AI-NN-PR] tenlei DL : 0: function [U,center,result,w,obj_fcn]= fenlei(data) [data_n,in_n] = size(data) m= 2 ％ Exponent for U max_iter = 100 ％ Max. iteration min_impro =1e-5 ％ Min. improvement c=3 [center, U, obj_fcn] = fcm(data, c) for i=1:max_iter if F(U)>0.98 break else w_new=eye(in_n,in_n) center1=sum(center)/c a=center1(1)./center1 deta=center-center1(ones(c,1),:) w=sqrt(sum(deta.^2)).*a for j=1:in_n w_new(j,j)=w(j) end data1=data*w_new [center, U, obj_fcn] = fcm(data1, c) center=center./w(ones(c,1),:) obj_fcn=obj_fcn/sum(w.^2) end end display(i) result=zeros(1,data_n) U_=max(U) for i=1:data_n for j=1:c if U(j,i)==U_(i) result(i)=j continue end end end -function [U, center, result, w, obj_fcn] = fenlei (data) [data_n, in_n] = size (data) m = 2 Exponent for U max_iter = 100 Max. iteration min_impro = 1e-5 Min. improvement c = 3 [center, U, obj_fcn] = fcm (data, c) for i = 1: max_iter if F (U)> 0.98 break else w_new = eye (in_n, in_n) center1 = sum (center)/ca = center1 (1) ./center1 deta = center-center1 (ones (c, 1),:) w = sqrt (sum (deta. ^ 2)) .* a for j = 1: in_n w_new (j, j) = w (j) end data1 = data* w_new [center, U, obj_fcn] = fcm (data1, c) center = center./w (ones (c, 1),:) obj_fcn = obj_fcn/sum (w. ^ 2) end end display (i) result = zeros (1, data_n) U_ = max (U) for i = 1: data_n for j = 1: c if U (j, i) == U_ (i) result (i) = j continue end end end
Date : 2025-12-29 Size : 3kb User : download99
[AI-NN-PR] getgrad DL : 0: Produces a matrix of derivatives of network output w.r.t. ％ each network weight for use in the functions NNPRUNE and NNFPE.-Produces a matrix of derivatives of network output wrt each network weight for use in the functions NNPRUNE and NNFPE.
Date : 2025-12-29 Size : 3kb User : 张镇
[AI-NN-PR] libsvm-2.88 DL : 0: 支撑向量机SVM的工具LIBSVM，能够在windows平台下通过命令行使用，也可以在matlab下调用，适合于研究复杂条件下的分类问题-Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM regression. It also provides an automatic model selection tool for C-SVM classification.
Date : 2025-12-29 Size : 506kb User : 雷源
[AI-NN-PR] dosjumper DL : 0: 利用c++编写的带人工智能的跳棋程序。屏幕的中央是棋盘，所有的操作都是对这个棋盘进行的，棋子的选择框是红色的，移动它（“w”、“s”、“a”、“d”分别代表上、下、左、右）进行选子和选择目的地，利用空格键可以表示选定选择框到达的位置，棋盘左边有提示信息，提示由哪个玩家走棋。“Q”表示退出游戏，“R”表示重新开始，“H”表示悔棋（一次只能悔一步棋）-Using c++, prepared with artificial intelligence Checkers procedures. Screen are the central board, all the operations are carried out on the chessboard, chess pieces of the selection box is red, move it ( " w" , " s" , " a" , " d" represent the upper and lower left , right) to carry out the election, and the selection of destination, using the space bar can be selected express selection box arrive the location, the left has a message board, indicating which player moves. " Q" exit express Game, " R" express a fresh start, " H" express悔棋(one can only regret move)
Date : 2025-12-29 Size : 94kb User : ccq
[AI-NN-PR] T-W DL : 0: 电厂风速和床温的数据,非常难得,希望大家珍藏!电厂风速和床温的数据,非常难得,希望大家珍藏!-Wind power and bed temperature data, is very rare, I hope everyone treasures! Plant bed temperature and wind speed data, a very rare, I hope everyone treasures!
Date : 2025-12-29 Size : 3kb User : liyan
[AI-NN-PR] TSPandMTSP DL : 0: MTSP 问题其实与单旅行商问题(Traveling Salesperson Problem ,简称TSP) 相似,但是由于添加了任何城市只要被某一旅行商访问到即可这个附加条件,因而增加了问题复杂度。在以前使用遗传算法(GA) 研究解决MTSP 问题时,通常采用标准的TSP 染色体和处理方法。-M any app licat ions are invo lved w ith mult ip le salesmen each of w hom visits a subgroup cit ies and returns the same start ing city. The to tal length of all subtours is required to be m ini2 mum. Th is is calledM ult ip le T raveling Salesmen P roblem (M TSP). There are various heurist ic methods to obtain op t imal o r near2op t imal so lut ions fo r the TSP p roblem. But to the M ult ip le T raveling Salesmen P roblem , there are no t much app roaches to so lveM TSP. In th is paper, a hy2 brid genet ic algo rithm to so lve TSP and M TSP is p resented. Th is algo rithm combines GA and heurist ics. N umerical experiments show that the new algo rithm is very efficient and effect ive.
Date : 2025-12-29 Size : 212kb User : liqiubin
[AI-NN-PR] box DL : 0: 由文件input.txt提供输入数据。输入文件第1 行有2个正整数n和m（1<=n,m<=100），表示仓库是n×m个格子的矩形阵列。接下来有n行，每行有m个字符，表示格子的状态。 S 表示格子上放了不可移动的沉重货物； w 表示格子空闲； M 表示仓库管理员的初始位置； P 表示箱子的初始位置； K 表示箱子的目标位置。 -Provided by the input data file input.txt. Line 1 input file there are two positive integers n and m (1 < = n, m < = 100), said the warehouse is a n × m rectangular grid array. Then there are n lines, each line has m characters, said the state lattice. Lattice S that can not be put on the movement of heavy goods w that free lattice M said that the initial location of the warehouse manager P said that the initial location of the box K said that the goal of the location of the box.
Date : 2025-12-29 Size : 3kb User : 王加福
[AI-NN-PR] Matlabeg DL : 0: 基于 Ma t l a b语言的遗传算法工具箱支持二进制和浮点数编码方式，并且提供了多种选择、交叉、变异的方法。通过具体实例对 Ma t l a b的遗传算法工具箱的用法进行了说明介绍.-The Ge ne t i c Al g or it h m To o l b ox ba s e d on Ma t l a b s u ppo ~s t h e b i na r y a nd f lo a t ， a n d t he r e a r e t he e x c el l e nl o pe r at o r s o f s el e c t i on ，c r os s o v e r a nd mut a t i on i n t he To o l bo x ，t wo e xa mpl e s a b o ut ho w t o us e t h e To o l bo x a r e i n t r o du c ec i n t h i s pa p er。
Date : 2025-12-29 Size : 98kb User : 阿铁
[AI-NN-PR] AHybidGeneticAgorithmtoSolveTSPandMTSP DL : 0: 求解TSP和MTSP的混合遗传算法_英文_-Abstract:M any app licat ions are invo lved w ith mult ip le salesmen each of w hom visits a subgroup cit ies and returns the same start ing city. The to tal length of all subtours is required to be m ini2 mum. Th is is calledM ult ip le T raveling Salesmen P roblem (M TSP). There are various heurist ic methods to obtain op t imal o r near2op t imal so lut ions fo r the TSP p roblem. But to the M ult ip le T raveling Salesmen P roblem , there are no t much app roaches to so lveM TSP. In th is paper, a hy2 brid genet ic algo rithm to so lve TSP and M TSP is p resented. Th is algo rithm combines GA and heurist ics. N umerical experiments show that the new algo rithm is very efficient and effect ive. Key words: TSP op t im izat ion genet ic algo rithm 2op t
Date : 2025-12-29 Size : 212kb User : Notics
[AI-NN-PR] steerfilter DL : 0: Design and Use of Steerable Filters PAMI1991文章的代码，含有测试图像和demo，可运行-It implements a steerable Gaussian filter. This m-file can be used to evaluate the first directional derivative of an image, using the method outlined in: W. T. Freeman and E. H. Adelson, "The Design and Use of Steerable Filters", IEEE PAMI, 1991.
Date : 2025-12-29 Size : 330kb User : 李
[AI-NN-PR] Dijkstra DL : 0: 用Dijkstra法求最短路径，有向图与无向图均可-void ShortestPath_DIJ( Node a ,Status i ,Status v0 ,Status*D ,Status*pre ) { int v,w,j,l=1 Status*final Status min final=(Status*)malloc( sizeof(Status)*i ) for(v=0 v<i v++) { final[v]=FALSE pre[v]=FALSE D[v]=a[v0][v] if(D[v]<10000) pre[v]=v0 }
Date : 2025-12-29 Size : 1kb User : 腾龙
[AI-NN-PR] Sense DL : 0: W-H算法和感知器算法，VC6下的一个基于对话框的实现了数据的分类小的程序-WH algorithm and the Perceptron Algorithm, VC6 under the implementation of a dialog-based data classification procedures for small-
Date : 2025-12-29 Size : 33kb User : 宋天明
[AI-NN-PR] CompStats DL : 0: 计算统计学工具箱，W. L. and A. R. Martinez 开发-Calculate statistical toolbox, WL and AR Martinez Development
Date : 2025-12-29 Size : 563kb User : 蔡涛
[AI-NN-PR] Hopfield DL : 0: implement a Hopfield network for the retrieval of stored 2-D patterns-Input Parameters: Ask the user to input the number of fundamental memories p (2 ≤ p ≤ 5), the width w (3 ≤ w ≤ 5) and height h (3 ≤ h ≤ 6) of each 2-D pattern. Storage Phase: Each fundamental memory is stored in a text file with .txt extension. Retrieval Phase: Ask the user to input the name of the text file storing the noisy pattern.
Date : 2025-12-29 Size : 4kb User : Owen Yin
[AI-NN-PR] IntelligentTacticalFlight DL : 0: &基于贝叶斯网络和模糊推理技术A实现了战场威胁级别及其相对重要性程度的综合评估&利用模型预测控制的滚动优化和在线校正原理A实现了飞机在线飞行路径规划&建立了路径规划代价函数中加权因子的智能化分配方法A进而实现了威胁评估与路径规划之间的集成A使得路径规划系统能够自适应战场态势的动态变化.-koorow&E}8k w$lrm}nz$pkzz8zzn8omp$lm}l8km 8y8 ko|l8 kmry8rnj$lmkos8kl88zmk9 rz}8|9kz8|$ouk{8zrko o8m*$l+zko|pq { $wrs&E}8p rw}mjkm}j koorowrzrnj 8n8om8|qzrowm}8n$|8 jl8|rsmry8s$oml$ |8j8o|row$o rmzl8s8|row}$lr $o$jmrnr kmr$oko|s$ll8smr$o$o< ro8&E}8rom8 rw8omkzzrwon8om$p*8rw}mrowpksm$lzros$zm pqosmr$o$pjkm}j koorowrz|8y8 $j8|9kz8|$opq { $wrsA*}rs}rom8wlkm8|zrmqkmr$okzz8zzn8om*rm}jkm} j koorow&E}8jl$j$z8|rom8 rw8ommksmrsk p rw}mjkm}j koorowz{zm8n skok|kjmm}8|{oknrsyklrk9 8zrmqkmr$ozro 9kmm 8pr8 |&E}8zrnq kmr$ol8zq mzz}$*m}8w$$|8pp8smry8o8zz
Date : 2025-12-29 Size : 133kb User : hans
[AI-NN-PR] Dijkstra DL : 0: 图与网络论中求最短路径的Dijkstra算法 M-函数格式 [S,D]=minroute(i,m,W) i为最短路径的起始点,m为图顶点数,W为图的带权邻接矩阵，不构成边的两顶点之间的权用inf表示。显示结果为：S的每一列从上到下记录了从始点到终点的最短路径所经顶点的序号； D是一行向量，记录了S中所示路径的大小 -Graph and network theory Dijkstra' s shortest path algorithm M-function the format [S, D] = minroute (i, m, W) i is the starting point of the shortest path, m is the graph vertices, W is the graph with adjacency matrix power, does not constitute the edge between two vertices of the right to use the inf that. Showing results: S a top to bottom of each record from the beginning to the end point of the shortest path through the vertices of the serial number D is a row vector to record the path shown in the size of S
Date : 2025-12-29 Size : 1kb User : 童康
[AI-NN-PR] Adaptive-Hysteresis DL : 0: 基于径向基函数神经网络迟滞非线性自适应控制提出了一种新的动态迟滞非线性模型. 将一定数量不同死区宽度的 backlash 模型并行相加, 作为一个动态系统以仿真执行器中的迟滞特性. 利用该模型, 采用伪控制方法设计了一套具有未知迟滞特性非线性系统的神经网络自适应控制方案, 通过自适应算法来调整干扰项的上限. 采用 Lyapunov 稳定性理论进行了严格证明, 仿真试验验证了所提方案的有效性.- A nov el class of hysteresis mo dels w as proposed. A cer tain num ber o f different deadband w idth backlash models are superposed, w hich represents a dynamics to m im ic hysteresis in the actuator. With the mo del proposed, an radial basis function neural netw ork ( RBFN )-based adaptive control scheme for nonlinear sy stems w ith unknow n hysteresis nonlinearity w as dev elo ped. The control scheme adopts the de- sign method of pseudo-co ntro l. Witho ut the assumption of boundedness of disturbance term , it is tuned thr oug h adaptive algo rithm . The stability is rigidly pr oved v ia Lyapunov theory and the effectiveness of the pro posed contr ol scheme is illustrated through simulatio n.
Date : 2025-12-29 Size : 207kb User :
[AI-NN-PR] SVM-w-SMO DL : 0: 用序列最小优化算法（SMO）进行训练的支持向量机的简单实现。(simple implementation of a Support Vector Machine using the Sequential Minimal Optimization (SMO) algorithm for training.)
Date : 2025-12-29 Size : 49kb User : brbaaa

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