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[AI-NN-PR] GAdownload DL : 0: 遗传求解求解一元二次方程的解源程序(下载点击GAdownload.c) 说明： 1 popu变量表示群体规模 2 L变量表示染色体的长度 3 pc,pm变量分别代表是交叉概率和变异概率 4 gen是迭代的代数 4 chromosome是一个全局的二维数组，里面存放的是个体的编码 5 程序最后的执行结果输出到了text.txt文本文件中-genetic solving quadratic equation to solve one yuan source solutions (click Download GAdownload.c) states : a 309 variables for population size two variables L said the length of chromosome 3 pc. pm variables were representatives of the cross-probability and the probability of four gen variation of the iterative algebra is chromosome 4 a generic two-dimensional array, stored inside the five individual coding procedures results of the implementation of the final output to a text file text.txt
Date : 2025-12-27 Size : 1kb User : 万飞
[AI-NN-PR] FuzzyLib450 DL : 0: 人工智能中模糊逻辑算法 FuzzyLib 2.0 is a comprehensive C++ Fuzzy Logic library for constructing fuzzy logic systems with multi-controller support. It supports all commonly used shape functions and hedges, with full support for the various types of Aggregation, Correlation, Alphacut, Composition, Defuzzification methods. The latest version of the C++ Fuzzy Logic Class Library contains all the C++ source code and comes complete with a usage example for building a multi-controllers fuzzy logic model.-artificial intelligence, fuzzy logic algorithm FuzzyLib 2.0 is a comprehensi 've Fuzzy Logic C library for constructing fuzzy logic systems with multi-controller support. It supports all commonly used functions a shape nd hedges. with full support for the various types of Aggre the accounts, Correlation, Alphacut, Composition, Defuzzification methods. The latest version o f the C Fuzzy Logic Class Library contains all th e C source code and comes complete with a usage ex ample for building a multi-fuzzy controllers l ogic model.
Date : 2025-12-27 Size : 307kb User : 周荷
[AI-NN-PR] automix DL : 0: he AutoMix package is a C program for Unix-like systems, implementing the automatic reversible jump MCMC sampler of the same name described in Chapters 4, 5, and 6 of David Hastie s Ph.D. thesis-he AutoMix package is a C program for Unix-l ike systems, implementing the automatic reversible jump MC MC sampler of the same name described in Chapter s 4, 5, and 6 of David Hastie's Ph.D. thesis
Date : 2025-12-27 Size : 91kb User : sjtuzyk
[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 : 2025-12-27 Size : 4kb User : rtshen
[AI-NN-PR] Hidden_Markov_model_for_automatic_speech_recogniti DL : 1: Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden Markov models.
Date : 2025-12-27 Size : 23kb User :
[AI-NN-PR] IncrementalRandomNeurons DL : 0: 本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,
Date : 2025-12-27 Size : 2kb User : chenlei
[AI-NN-PR] moshishibie DL : 1: 先用C-均值聚类算法程序，并用下列数据进行聚类分析。在确认编程正确后，采用蔡云龙书的附录B中表1的Iris数据进行聚类。然后使用近邻法的快速算法找出待分样本X（设X样本的4个分量x1=x2=x3=x4=6；子集数l=3）的最近邻节点和3-近邻节点及X与它们之间的距离。-First C-means clustering algorithm procedures and with the following data for cluster analysis. After confirming the correct programming, using the book蔡云龙Table 1 in Appendix B of the Iris data clustering. And the use of a close neighbor of law to be fast algorithm to find sub-samples of X (for X samples four components x1 = x2 = x3 = x4 = 6 subset of the number of l = 3) of the nearest neighbor nodes and 3- neighbor nodes and X and the distance between them.
Date : 2025-12-27 Size : 1kb User : jack
[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 : 2025-12-27 Size : 98kb User : 阿铁
[AI-NN-PR] JKLMNOPMQRONSQMTOULUVRONWQXMLTRKRTXNR DL : 0: 蚁群算法是一类模拟生物群体突现聚集行为的非经典算法E首先描述了一个简单蚂蚁系统及其简单蚁群算法C并对其进行了计算机程序模拟与动力系统仿真E结果表明C简单蚂蚁系统中存在规模聚集效应C当蚁群的规模超过某一临界值时C蚂蚁的行为开始向有序的方向收敛C并最终稳定在一种有序状态E 关键词=蚂蚁系统F蚁群算法F仿真F多主体系统中 -Ihl &o jmlskq h & omxplspq }m &~ps vm rj i hl} & hmpq’Iqmk!&p hlq<qlpk "mlsb i j omho hlq h# hpqs jlpj! ls h# hp& hopj! lsls l&p #l lspq kpi #q rj}pmq#pq}jm~p#’I qmk!&p hl &o jmlskmqomvph’G h &<xplsppkpjophl oojpo lm h!j !pjl< ilspqmk!&p hlq<qlpk
Date : 2025-12-27 Size : 229kb User : fhyang
[AI-NN-PR] SnOW DL : 0: This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes: Conditional Maximum Entropy Model L-BFGS Parameter Estimation GIS Parameter Estimation Gaussian Prior Smoothing C++ API Python Extension module Document and Tutorial -)-This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes: Conditional Maximum Entropy Model L-BFGS Parameter Estimation GIS Parameter Estimation Gaussian Prior Smoothing C++ API Python Extension module Document and Tutorial -)
Date : 2025-12-27 Size : 747kb User : shabo
[AI-NN-PR] Antibiotic-Therapy.pdf.tar DL : 0: List the factors that a clinician should follow to select an appropriate antimicrobial regimen. • C Compare and contrast the normal b d t d t t th l body temperature when thmeasured orally, rectally, or axillary. axillary. • Describe the changes in the white blood cell count indicative of bacterial, viral, or fungal infections. • List the reasons for obtaining microbiology samples (i.e., blood cultures, sputum, urine, etc.) before the institution of antimicrobial therapy. • List the important host factors that must be considered when p choosing an antimicrobial regimen for a patient Name two characteristics of penicillin allergy that indicate cephalosporins and carbapenems should be avoided Identify major drug-drug interactions for commonly prescribed drug anti-infectivec anti-infectives- List the factors that a clinician should follow to select an appropriate antimicrobial regimen. • C Compare and contrast the normal b d t d t t th l body temperature when thmeasured orally, rectally, or axillary. axillary. • Describe the changes in the white blood cell count indicative of bacterial, viral, or fungal infections. • List the reasons for obtaining microbiology samples (i.e., blood cultures, sputum, urine, etc.) before the institution of antimicrobial therapy. • List the important host factors that must be considered when p choosing an antimicrobial regimen for a patient Name two characteristics of penicillin allergy that indicate cephalosporins and carbapenems should be avoided Identify major drug-drug interactions for commonly prescribed drug anti-infectivec anti-infectives
Date : 2025-12-27 Size : 225kb User : akram
[AI-NN-PR] Program-for-the-perfect-lens DL : 0: 采用在传统传输线上周期性地加载串联电容C和并联电感L构造了一种二维负折射率传输线微波媒质，不加载L和C的传输线单元则组成正折射率传输线微波媒质，由这两种煤质构成了一个完美透镜系统。使用Bloch理论，导出了这种正负折射率媒质的色散关系和布洛克(Bloch)阻抗,并且计算出了在由点源在各个区域产生的分布电压幅度、相位- the traditional transmission line periodically loaded parallel series capacitor C and inductance L consist of a class of two-dimensional microwave transmission line negative refractive index medium, the medium which do not load L and C of the transmission line unit consists of positive refractive index medium microwave transmission line, the two kinds of medium above form a perfect lens system. Based on Bloch theory, the dispersion relations and Bloch (Bloch) impedance of positive and negative refractive index is derived. And voltage amplitude which is produced by the point source in various regions is calculated
Date : 2025-12-27 Size : 3kb User : 李世成
[AI-NN-PR] CLIPS_6.30_Beta.R3 DL : 0: clips最新版　　CLIPS是一种专家系统工具，最初由NASA/Lyndon B. Johnson太空中心软件技术研究室开发出来。自1986年首次发布以来，CLIPS经历了不断的改进和完善。现在它已经被广泛的应用在数以万计的全球用户中。　　CLIPS被开发出来以促进集成人类知识和经验的软件发展。　　在CLIPS中，知识的表示有三种方式：　　l 规则，规则表示法是基于启发式经验知识库的首要选择。　　l 自定义函数和通用函数，这种方式是程序式知识表示的首选。　　l 面向对象设计，也是程序式知识表示的首选。面向对象的程序设计被支持的5个普遍接受的特征是：类，消息处理函数，抽象，封装，继承和多态性。模式匹配可以是对象和事实。　　你可以仅用规则，或者仅用对象或者两者混合使用来开发软件。　　CLIPS同时支持与其他语言的集成，如C和Java。事实上，CLIPS是C Language Integrated Production的缩写。规则能基于事实与对象的匹配，规则和对象同时组成了一个集成系统。除了被当作一个独立的工具之外，CLIPS还能被程序语言调用，运行其函数，然后返回给调用函数控制权。同样的，程序代码也能作为一个外部函数在CLIPS中被定义和调用。当外部代码执行完毕后，控制权返回到CLIPS -CLIPS is an expert system tool, originally developed by the Software Technology Laboratory of the NASA/Lyndon B. Johnson Space Center. Since its first release in 1986, CLIPS has undergone continuous improvement and perfection. It is now widely used in tens of thousands of users worldwide. CLIPS was developed to facilitate the integration of human knowledge and experience in software development. In CLIPS, knowledge representation in three ways: l rules, rules, notation is the first choice of heuristic experience-based knowledge base. l-defined functions and general functions, this approach is the first choice of the procedural knowledge. l object-oriented design, is also the program of choice for knowledge representation. The five generally accepted features of object-oriented programming support: classes, message-handling functions, abstraction, encapsulation, inheritance and polymorphism. Pattern matching can be the objects and facts. You can only rule, or only to develop the softwar
Date : 2025-12-27 Size : 6.76mb User : 魏林
[AI-NN-PR] chess_fugai_mfc DL : 0: 在visual c++6.0环境下实现使用L形骨牌进行棋盘覆盖问题。-In visual c++ 6.0 environment using the L-shaped dominoes board cover.
Date : 2025-12-27 Size : 14kb User : weareonenow
[AI-NN-PR] lunwen DL : 0: 新一代高性能无人机飞控系统的研究与设计张小林赵宇博范力思-I n o r de r t o cau se t he U A V f lig ht co nt r o l sy st e m has t he f o r mida ble da t a- ha ndling ca pa cit y , t h e lo w po we r lo ss , t he st r o ng f le x ibilit y an d a hig he r int e g r at io n r a t e, pr o po sed o ne kind of t e chn ol og y ba sed on SO P C w hic h ca n so lv e t hes e p r ob lem s o n U A V f lig ht co n t r o l sy st e m. T r a nsf e r s m any N io s so f t pr o c ess o r . A lt e r a br in gi ng t he I P co r e a s w el l as t he per ip her y ha r dw a r e cir c uit . on e kind o f h ig h per f o r man ce f lig ht co nt r o l sy st em h as be en de sig ned. Co mpa r e s w it h t he t r a diti on al U A V co nt r o l sy s te m, t his o ne hav e ve r y st r o ng dat a handl ing capa cit y , t h e sma ll v o lume a nd lo w po w e r lo s s. T he a ct ua l fl ig ht r esul t indic at e d : Ea ch m odu la r de sig n is r ea so nab le, t he o ve r al l sy st em mo v e ment is st a ble. T his sy st e m ca n b e ser ve d a s t he ne x t g ene r
Date : 2025-12-27 Size : 606kb User : 天下
[AI-NN-PR] ye_ren_chuan_jiao_si_guo_he DL : 0: 对N＝5、k≤3时，求解传教士和野人问题的产生式系统各组成部分进行描述（给出综合数据库、规则集合的形式化描述，给出初始状态和目标条件的描述），并画出状态空间图。答：用M表示传教士，C表示野人，B表示船，L表示左岸，R表示右岸。-For N = 5, k ≤ 3, the missionaries and Savage problem solving production system components are described (given comprehensive database rule set formal description of the initial state and the target given the conditions described), and draw the state space diagram. A: M for missionaries, C represents Savage, B represents boat, L is left, R represents the right bank.
Date : 2025-12-27 Size : 424kb User : pw
[AI-NN-PR] MOEA-NSGA-II DL : 0: NSGA (No n- Do mina te d So r ting in Ge ne tic Alg o r ithms [5 ]) is a p o pula r no n-do mina tio n ba s e d g e ne tic a lg o r ithm fo r multi- o b je c tive o ptimiz a tio n. I t is a ve r y e ﬀ e c tive a lg o r ithm but ha s b e e n g e ne r a lly c r itic iz e d fo r its c o mputa tio na l c o m-ple x ity, la ck o f e litis m a nd fo r cho o s ing the o ptima l pa r a mete r va lue fo r s ha r ing pa r a me te r σsh ar e . A mo diﬁ e d ve r s io n, NSGA- I I ( [3 ]) wa s de ve lo p e d, w hich ha s a b e tte r s o r ting a lg o r ithm , inc o r p o r a te s e litis m a nd no s ha r ing pa r a me te r ne e ds to b e cho s e n a priori. NSGA- I I is dis c us s e d in de ta il in this r e p o r t a nd two s a mple te s t func tio ns a r e o ptimiz e d us ing it.
Date : 2025-12-27 Size : 363kb User : wenxiaoyong
[AI-NN-PR] 0-svnn DL : 0: 这段代码实现了一个新的MLP神经网络训练方法,来自论文A new method for neural network regularization（内附）-This code implements a new training method for MLP neural networks, named Support Vector Neural Network (SVNN), proposed in the work: O. Ludwig “Study on Non-parametric Methods for Fast Pattern Recognition with Emphasis on Neural Networks and Cascade Classifiers ” PhD Thesis, University of Coimbra, Coimbra, 2012. The input arguments are a N x L matrix of L representative N-element input vectors, a row vector, y, whose elements are the respective target classes, which should be-1 or 1, and the number of hidden neurons, nneu. Similarly to SVMs, the SVNN has a punishing parameter, C, which can be set in the line 16 of the code. The algorithm outputs the MLP parameters, W1, W2, b1, b2, which are input arguments of the MLP simulator “sim_NN.m” that also requires the matrix of testing data, as well as the target vector (in case of target unavailable, a empty vector must be supplied). “sim_NN.m” outputs the estimated class and the accuracy, acc (when testing targets are available). The code
Date : 2025-12-27 Size : 2.92mb User : 孙园