Frequently Asked Questions matlab introduced for the artificial neural network B...

这个程序是个神经网络提供学习能力的实例，是前一个simpleann的功能加强版。-this procedure is a neural network learning ability example is the former simpleann an enhanced version of the function.

包含FuzzyKMeans, FuzzyGK, FuzzyR等算法，拓展了weka（本站可以下载）-Contains algorithm such as FuzzyKMeans, FuzzyGK, FuzzyR. Extends weka(it is can be download form this site)

计算机故障智能诊断系统 用户输入计算机的故障症状 即可由系统得出故障原因和相关的处理方法-Diagnose the system in computer s trouble intelligence Users input the trouble symptom of the computer Can be had cranky reason and relevant treatment methods systematically

使用BP网络进行模式识别,识别0－9的数字-Using the BP network pattern recognition, identifying the number of 0-9

nsga2非支配排序遗传算法，c++源码实现-nsga2 non-dominated sorting genetic algorithm, c++ source code to achieve

10。《用于最优化的计算智能》，Nirwan Ansali，Edwin Hou着，李军，边肇棋译 清华大学出版社 1999年第一版 本书从讨论组合优化中的基本问题――NP问题入手，系统地讲述了近年来所发展起来的智能最优化的各种技术和方法，其中包括启发式搜索、Hopfield神经网络、模拟退火和随机机、均场退火以及遗传算法等；并在此基础上，通过一些典型的应用问题，如旅行商问题、模式识别中的点模式匹配问题、通信和任务调度等问题进一步阐明以上一些基本方法怎样用来解决这些原来具有NP性质的困难问题。本书是作者在美国新泽西州理工学院多年讲授有关课程的基础上写成的。全书深入浅出，理论联系实际。为帮助学生掌握基本概念，提高学习能动性，各章编写了习题。本书可作为通信、计算机、控制各专业的高年级学生和研究生学习有关课程的教材。它对于广大科研工作者也是一本很有实际价值的参考书。 -10. "Optimal for Computational Intelligence," Nirwan Ansali, with Edwin Hou, Li Jun, Pin Zhaoxing chess translation Tsinghua University Press in 1999 the first book version of combinatorial optimization from the discussion of basic questions-- NP problem start, the system described in recent years to develop the intelligent optimization of the technology and methods, These include heuristic search, Hopfield networks, simulated annealing and random machine, mean field annealing and genetic algorithms; and on this basis and through some typical applications, such as the traveling salesman probl

最大概率分词法,这种分词算法能够较好的解决汉语分词中的歧义问题,但分词效率比最大匹配分词算法要低-greatest probability points accidence, Segmentation algorithm can be used to solve the Chinese word segmentation of Ambiguity, but Word efficient than the largest matching segmentation algorithm lower

SMC1602A(16*2)模拟口线接线方式 连接线图: --------------------------------------------------- |LCM-----51 | LCM-----51 | LCM------51 | --------------------------------------------------| |DB0-----P0.0 | DB4-----P0.4 | RW-------P2.5 | |DB1-----P0.1 | DB5-----P0.5 | RS-------P2.6 | |DB2-----P0.2 | DB6-----P0.6 | E--------P2.7 | |DB3-----P0.3 | DB7-----P0.7 | VLCD接1K电阻到GND| --------------------------------------------------- Keyboard接线 PS/2--------51 1 DATA------P3.4 3 GND 4 VCC 5 CLK-------P3.3 接在51的外部中断，触发方式为低电平 本程序源码只供学习参考，不得应用于商业用途，如有需要请联系作者。-SMC1602A (16* 2) analog line port to connect wiring diagram:- | LCM-51 | LCM-51 | LCM 51 | | | DB0-P0.0 | DB4-P0.4 | RW-P2.5 | | DB1-P0.1 | DB5-P0.5 | RS-P2.6 | | DB2-P0.2 | DB6-P0.6 | E P2.7 | | DB3-P0.3 | DB7-P0.7 | VLCD access 1K resistor to GND |-Keyboard Connection PS/2 51 1 DATA P3.4 3 GND 4 VCC 5 CLK-P3.3 access in 51 of the external interrupt trigger mode for low-level source for learning the procedu

人工智能课程中关于产生式系统部分上机实验的源代码，有很大参考价值。-No

提出了基于最小化估计的泛化误差界非优化估计方法。目前大多数的核参数选择方法都是通过极小化了来得到最优参数值，但是求解优化问题的计算代价相当的大，并且不能很好地体现数据的分布特征。本文采用非优化技术，通过极小化泛化误差来优化核及相关参数，由于直接计算最小半径和最大间隔，避免了对优化问题的直接求解，因此可以很好地降低计算代价。并且该方法直接从样本出发，可以很好地体现数据的分布特征，不管数据分布是否均匀都可以适用。给出了基于凸包估计的SVM核选择的模型及实现算法。 -Proposed based on minimizing the estimated generalization error bound of the non-optimal estimation method. Most of the nuclear parameter selection methods are to get the best by minimizing the parameter values, but the computational cost for solving optimization problems is quite large, and can not properly reflect the distribution characteristics of the data. In this paper, the non-optimization technology, by minimizing the generalization error to optimize the nuclear and related parameters, due to the direct calculation of the minimum radius and maximum interval, avoiding the direct solution of the optimization problem, it can very well reduce the computation cost. And that the method directly from the sample one can well reflect the distribution chara