<html xmlns="http://www.w3.org/1999/xhtml"><head><meta charset="utf-8"><meta name="generator" content="pdf2htmlEX"><meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"><link rel="stylesheet" href="https://csdnimg.cn/release/download_crawler_static/css/base.min.css"><link rel="stylesheet" href="https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css"><link rel="stylesheet" href="https://csdnimg.cn/release/download_crawler_static/9676963/raw.css"><script src="https://csdnimg.cn/release/download_crawler_static/js/compatibility.min.js"></script><script src="https://csdnimg.cn/release/download_crawler_static/js/pdf2htmlEX.min.js"></script><script>try{pdf2htmlEX.defaultViewer = new pdf2htmlEX.Viewer({});}catch(e){}</script><title></title></head><body><div id="sidebar" style="display: none"><div id="outline"></div></div><div id="pf1" class="pf w0 h0" data-page-no="1"><div class="pc pc1 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="https://csdnimg.cn/release/download_crawler_static/9676963/bg1.jpg"><div class="c x0 y1 w2 h2"><div class="t m0 x1 h3 y2 ff1 fs0 fc0 sc0 ls0 ws0">MA<span class="_ _0"></span>TLAB <span class="ff2 sc1">代码</span></div><div class="t m0 x2 h3 y3 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">1<span class="_"> </span></span>章 <span class="ff1 sc0">BP<span class="_ _1"> </span></span>神经网络的数据分类——语音特征信号分类</div><div class="t m0 x2 h3 y4 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">2<span class="_"> </span></span>章 <span class="ff1 sc0">BP<span class="_ _1"> </span></span>神经网络的非线性系统建模——非线性函数拟合</div><div class="t m0 x2 h3 y5 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">3<span class="_"> </span></span>章 遗传算法优化<span class="_ _1"> </span><span class="ff1 sc0">BP<span class="_ _1"> </span></span>神经网络——非线性函数拟合</div><div class="t m0 x2 h3 y6 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">4<span class="_"> </span></span>章 神经网络遗传算法函数极值寻优——非线性函数极值寻优</div><div class="t m0 x2 h3 y7 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">5<span class="_"> </span></span>章 基于<span class="_ _1"> </span><span class="ff1 sc0">BP_Adab<span class="_ _2"></span>oost<span class="_ _1"> </span><span class="ff2 sc1">的强分类器设计——公司财务预警建模</span></span></div><div class="t m0 x2 h3 y8 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">6<span class="_"> </span></span>章 <span class="ff1 sc0">PID<span class="_ _1"> </span></span>神经元网络解耦控制算法——多变量系统控制</div><div class="t m0 x2 h3 y9 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">7<span class="_"> </span></span>章 <span class="ff1 sc0">RBF<span class="_ _1"> </span></span>网络的回归<span class="ff1 sc0">--</span>非线性函数回归的实现</div><div class="t m0 x2 h3 ya ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">8<span class="_"> </span></span>章 <span class="ff1 sc0">GRNN<span class="_ _1"> </span></span>网络的预测<span class="ff1 sc0">----</span>基于广义回归神经网络的货运量预测</div><div class="t m0 x2 h3 yb ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">9<span class="_"> </span></span>章 离散<span class="_ _1"> </span><span class="ff1 sc0">Ho<span class="_ _2"></span>p!eld<span class="_"> </span><span class="ff2 sc1">神经网络的联想记忆——数字识别</span></span></div><div class="t m0 x2 h3 yc ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">10<span class="_"> </span></span>章 离散<span class="_ _1"> </span><span class="ff1 sc0">Ho<span class="_ _2"></span>p!eld<span class="_"> </span><span class="ff2 sc1">神经网络的分类——高校科研能力评价</span></span></div><div class="t m0 x2 h3 yd ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">11<span class="_"> </span></span>章 连续<span class="_ _1"> </span><span class="ff1 sc0">Ho<span class="_ _2"></span>p!eld<span class="_"> </span><span class="ff2 sc1">神经网络的优化——旅行商问题优化计算</span></span></div><div class="t m0 x2 h3 ye ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">12<span class="_"> </span></span>章 初始<span class="_ _1"> </span><span class="ff1 sc0">S<span class="_ _2"></span>VM<span class="_ _1"> </span><span class="ff2 sc1">分类与回归</span></span></div><div class="t m0 x2 h3 yf ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">13<span class="_"> </span></span>章 <span class="ff1 sc0">L<span class="_ _2"></span>IBSVM<span class="_ _1"> </span><span class="ff2 sc1">参数实例详解</span></span></div><div class="t m0 x2 h3 y10 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">14<span class="_"> </span></span>章 基于<span class="_ _1"> </span><span class="ff1 sc0">S<span class="_ _2"></span>VM<span class="_ _1"> </span><span class="ff2 sc1">的数据分类预测——意大利葡萄酒种类识别</span></span></div><div class="t m0 x2 h3 y11 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">15<span class="_"> </span></span>章 <span class="ff1 sc0">S<span class="_ _2"></span>VM<span class="_ _1"> </span><span class="ff2 sc1">的参数优化——如何更好的提升分类器的性能</span></span></div><div class="t m0 x2 h3 y12 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">16<span class="_"> </span></span>章 基于<span class="_ _1"> </span><span class="ff1 sc0">S<span class="_ _2"></span>VM<span class="_ _1"> </span><span class="ff2 sc1">的回归预测分析——上证指数开盘指数预测</span>.</span></div><div class="t m0 x2 h3 y13 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">17<span class="_"> </span></span>章 基于<span class="_ _3"> </span><span class="ff1 sc0">SVM<span class="_ _1"> </span></span>的信息粒<span class="_ _4"></span>化时序<span class="_ _4"></span>回归预测<span class="_ _4"></span>——上<span class="_ _4"></span>证指数开<span class="_ _4"></span>盘指数变<span class="_ _4"></span>化趋势<span class="_ _4"></span>和变化空<span class="_ _4"></span>间预</div><div class="t m0 x2 h3 y14 ff2 fs0 fc0 sc1 ls0 ws0">测</div><div class="t m0 x2 h3 y15 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">18<span class="_"> </span></span>章 基于<span class="_ _1"> </span><span class="ff1 sc0">S<span class="_ _2"></span>VM<span class="_ _1"> </span><span class="ff2 sc1">的图像分割</span>-<span class="ff2 sc1">真彩色图像分割</span></span></div><div class="t m0 x2 h3 y16 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">19<span class="_"> </span></span>章 基于<span class="_ _1"> </span><span class="ff1 sc0">S<span class="_ _2"></span>VM<span class="_ _1"> </span><span class="ff2 sc1">的手写字体识别</span></span></div><div class="t m0 x2 h3 y17 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">20<span class="_"> </span></span>章 <span class="ff1 sc0">L<span class="_ _2"></span>IBSVM-F<span class="_ _2"></span>arutoUl+mat<span class="_ _2"></span>e<span class="_ _1"> </span><span class="ff2 sc1">工具箱及<span class="_ _1"> </span></span>GUI<span class="_ _1"> </span><span class="ff2 sc1">版本介绍与使用</span></span></div><div class="t m0 x2 h3 y18 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">21<span class="_"> </span></span>章 自组织竞争网络在模式分类中的应用—患者癌症发病预测</div><div class="t m0 x2 h3 y19 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">22<span class="_"> </span></span>章 <span class="ff1 sc0">SOM<span class="_ _1"> </span></span>神经网络的数据分类<span class="ff1 sc0">--</span>柴油机故障诊断</div><div class="t m0 x2 h3 y1a ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">23<span class="_"> </span></span>章 <span class="ff1 sc0">Elm<span class="_ _2"></span>an<span class="_"> </span><span class="ff2 sc1">神经网络的数据预测</span>----<span class="ff2 sc1">电力负荷预测模型研究</span></span></div><div class="t m0 x2 h3 y1b ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">24<span class="_"> </span></span>章 概率神经网络的分类预测<span class="ff1 sc0">--</span>基于<span class="_ _1"> </span><span class="ff1 sc0">PNN<span class="_ _1"> </span></span>的变压器故障诊断</div><div class="t m0 x2 h3 y1c ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">25<span class="_"> </span></span>章 基于<span class="_ _1"> </span><span class="ff1 sc0">MIV<span class="_ _1"> </span></span>的神经网络变量筛选<span class="ff1 sc0">----</span>基于<span class="_ _1"> </span><span class="ff1 sc0">BP<span class="_ _1"> </span></span>神经网络的变量筛选</div><div class="t m0 x2 h3 y1d ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">26<span class="_"> </span></span>章 <span class="ff1 sc0">L<span class="_ _0"></span>V<span class="_ _2"></span>Q<span class="_ _1"> </span><span class="ff2 sc1">神经网络的分类——乳腺肿瘤诊断</span></span></div><div class="t m0 x2 h3 y1e ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">27<span class="_"> </span></span>章 <span class="ff1 sc0">L<span class="_ _0"></span>V<span class="_ _2"></span>Q<span class="_ _1"> </span><span class="ff2 sc1">神经网络的预测——人脸朝向识别</span></span></div><div class="t m0 x2 h3 y1f ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">28<span class="_"> </span></span>章 决策树分类器的应用研究——乳腺癌诊断</div><div class="t m0 x2 h3 y20 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">29<span class="_"> </span></span>章 极限学习机在回归拟合及分类问题中的应用研究——对比实验</div><div class="t m0 x2 h3 y21 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">30<span class="_"> </span></span>章 基于随机森林思想的组合分类器设计——乳腺癌诊断</div><div class="t m0 x2 h3 y22 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">31<span class="_"> </span></span>章 思维进化算法优化<span class="_ _1"> </span><span class="ff1 sc0">BP<span class="_ _1"> </span></span>神经网络——非线性函数拟合</div><div class="t m0 x2 h3 y23 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">32<span class="_"> </span></span>章 小波神经网络的时间序列预测——短时交通流量预测</div><div class="t m0 x2 h3 y24 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">33<span class="_"> </span></span>章 模糊神经网络的预测算法——嘉陵江水质评价</div><div class="t m0 x2 h3 y25 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">34<span class="_"> </span></span>章 广义神经网络的聚类算法——网络入侵聚类</div><div class="t m0 x2 h3 y26 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">35<span class="_"> </span></span>章 粒子群优化算法的寻优算法——非线性函数极值寻优</div><div class="t m0 x2 h3 y27 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">36<span class="_"> </span></span>章 遗传算法优化计算——建模自变量降维</div><div class="t m0 x2 h3 y28 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">37<span class="_"> </span></span>章 基于灰色神经网络的预测算法研究——订单需求预测</div><div class="t m0 x2 h3 y29 ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">38<span class="_"> </span></span>章 基于<span class="_ _1"> </span><span class="ff1 sc0">K<span class="_ _2"></span>ohonen<span class="_ _1"> </span><span class="ff2 sc1">网络的聚类算法——网络入侵聚类</span></span></div><div class="t m0 x2 h3 y2a ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">39<span class="_"> </span></span>章 神经网络<span class="_ _1"> </span><span class="ff1 sc0">GUI<span class="_ _1"> </span></span>的实现——基于<span class="_ _1"> </span><span class="ff1 sc0">G<span class="_ _2"></span>UI<span class="_"> </span><span class="ff2 sc1">的神经网络拟合、模式识别、聚类</span></span></div><div class="t m0 x2 h3 y2b ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">40<span class="_"> </span></span>章 动态神经网络时间序列预测研究——基于<span class="_ _1"> </span><span class="ff1 sc0">MA<span class="_ _0"></span>TL<span class="_ _2"></span>AB<span class="_"> </span><span class="ff2 sc1">的<span class="_ _1"> </span></span>NARX<span class="_ _1"> </span><span class="ff2 sc1">实现</span></span></div><div class="t m0 x2 h3 y2c ff2 fs0 fc0 sc1 ls0 ws0">第<span class="_ _1"> </span><span class="ff1 sc0">41<span class="_"> </span></span>章 定制神经网络的实现——神经网络的个性化建模与仿真</div></div></div><div class="pi" data-data='{"ctm":[1.611850,0.000000,0.000000,1.611850,0.000000,0.000000]}'></div></div></body></html>
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