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<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://static.pudn.com/prod/directory_preview_static/6264ae004f8811599e62787f/bg1.jpg"><div class="c x0 y1 w2 h2"><div class="t m0 x1 h3 y2 ff1 fs0 fc0 sc0 ls0 ws0">《<span class="ff2 sc1">MA<span class="_ _0"></span>TLAB <span class="ff1 sc0">神经网络<span class="_ _1"> </span></span>43<span class="_"> </span><span class="ff1 sc0">个案例分析》目录</span></span></div><div class="t m0 x2 h3 y3 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">1<span class="_ _1"> </span></span>章 <span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">2<span class="_ _1"> </span></span>章 <span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">3<span class="_ _1"> </span></span>章 遗传算法优化<span class="_ _1"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">4<span class="_ _1"> </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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">5<span class="_ _1"> </span></span>章 基于<span class="_ _1"> </span><span class="ff2 sc1">BP_Adaboos<span class="_ _2"></span>t<span class="_"> </span><span class="ff1 sc0">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">6<span class="_ _1"> </span></span>章 <span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">7<span class="_ _1"> </span></span>章 <span class="ff2 sc1">RBF<span class="_ _1"> </span></span>网络的回归<span class="ff2 sc1">--</span>非线性函数回归的实现</div><div class="t m0 x2 h3 ya ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">8<span class="_ _1"> </span></span>章 <span class="ff2 sc1">GRNN<span class="_ _1"> </span></span>网络的预测<span class="ff2 sc1">----</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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">9<span class="_ _1"> </span></span>章 离散<span class="_ _1"> </span><span class="ff2 sc1">Hop<span class="_ _2"></span>!eld<span class="_"> </span><span class="ff1 sc0">神经网络的联想记忆——数字识别</span></span></div><div class="t m0 x2 h3 yc ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">10<span class="_ _1"> </span></span>章 离散<span class="_ _1"> </span><span class="ff2 sc1">Hop<span class="_ _2"></span>!eld<span class="_"> </span><span class="ff1 sc0">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">11<span class="_ _1"> </span></span>章 连续<span class="_ _1"> </span><span class="ff2 sc1">Hop<span class="_ _2"></span>!eld<span class="_"> </span><span class="ff1 sc0">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">12<span class="_ _1"> </span></span>章 初始<span class="_ _1"> </span><span class="ff2 sc1">SV<span class="_ _2"></span>M<span class="_"> </span><span class="ff1 sc0">分类与回归</span></span></div><div class="t m0 x2 h3 yf ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">13<span class="_ _1"> </span></span>章 <span class="ff2 sc1">LIBSVM<span class="_ _1"> </span></span>参数实例详解</div><div class="t m0 x2 h3 y10 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">14<span class="_ _1"> </span></span>章 基于<span class="_ _1"> </span><span class="ff2 sc1">SV<span class="_ _2"></span>M<span class="_"> </span><span class="ff1 sc0">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">15<span class="_ _1"> </span></span>章 <span class="ff2 sc1">SVM<span class="_ _1"> </span></span>的参数优化——如何更好的提升分类器的性能</div><div class="t m0 x2 h3 y12 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">16<span class="_ _1"> </span></span>章 基于<span class="_ _1"> </span><span class="ff2 sc1">SV<span class="_ _2"></span>M<span class="_"> </span><span class="ff1 sc0">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">17<span class="_"> </span></span>章 基于<span class="_ _3"> </span><span class="ff2 sc1">SVM<span class="_ _1"> </span></span>的信息粒<span class="_ _4"></span>化时序<span class="_ _4"></span>回归<span class="_ _5"></span>预测<span class="_ _4"></span>——上<span class="_ _4"></span>证指数<span class="_ _4"></span>开盘<span class="_ _4"></span>指数变<span class="_ _4"></span>化趋势<span class="_ _4"></span>和变化<span class="_ _4"></span>空间<span class="_ _4"></span>预</div><div class="t m0 x2 h3 y14 ff1 fs0 fc0 sc0 ls0 ws0">测</div><div class="t m0 x2 h3 y15 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">18<span class="_ _1"> </span></span>章 基于<span class="_ _1"> </span><span class="ff2 sc1">SV<span class="_ _2"></span>M<span class="_"> </span><span class="ff1 sc0">的图像分割</span>-<span class="ff1 sc0">真彩色图像分割</span></span></div><div class="t m0 x2 h3 y16 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">19<span class="_ _1"> </span></span>章 基于<span class="_ _1"> </span><span class="ff2 sc1">SV<span class="_ _2"></span>M<span class="_"> </span><span class="ff1 sc0">的手写字体识别</span></span></div><div class="t m0 x2 h3 y17 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _1"> </span><span class="ff2 sc1">20<span class="_ _1"> </span></span>章 <span class="ff2 sc1">LIBSVM-F<span class="_ _2"></span>arut<span class="_ _2"></span>oUl+mate<span class="_ _1"> </span><span class="ff1 sc0">工具箱及<span class="_ _6"> </span></span>GUI<span class="_ _6"> </span><span class="ff1 sc0">版本介绍与使用</span></span></div><div class="t m0 x2 h3 y18 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">22<span class="_"> </span></span>章 <span class="ff2 sc1">SOM<span class="_ _6"> </span></span>神经网络的数据分类<span class="ff2 sc1">--</span>柴油机故障诊断</div><div class="t m0 x2 h3 y1a ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">23<span class="_"> </span></span>章 <span class="ff2 sc1">Elman<span class="_ _6"> </span></span>神经网络的数据预测<span class="ff2 sc1">----</span>电力负荷预测模型研究</div><div class="t m0 x2 h3 y1b ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">24<span class="_"> </span></span>章 概率神经网络的分类预测<span class="ff2 sc1">--</span>基于<span class="_ _6"> </span><span class="ff2 sc1">PNN<span class="_"> </span></span>的变压器故障诊断</div><div class="t m0 x2 h3 y1c ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">25<span class="_"> </span></span>章 基于<span class="_ _6"> </span><span class="ff2 sc1">MIV<span class="_"> </span></span>的神经网络变量筛选<span class="ff2 sc1">----</span>基于<span class="_ _6"> </span><span class="ff2 sc1">BP<span class="_"> </span></span>神经网络的变量筛选</div><div class="t m0 x2 h3 y1d ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">26<span class="_"> </span></span>章 <span class="ff2 sc1">L<span class="_ _0"></span>V<span class="_ _2"></span>Q<span class="_"> </span><span class="ff1 sc0">神经网络的分类——乳腺肿瘤诊断</span></span></div><div class="t m0 x2 h3 y1e ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">27<span class="_"> </span></span>章 <span class="ff2 sc1">L<span class="_ _0"></span>V<span class="_ _2"></span>Q<span class="_"> </span><span class="ff1 sc0">神经网络的预测——人脸朝向识别</span></span></div><div class="t m0 x2 h3 y1f ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">31<span class="_"> </span></span>章 思维进化算法优化<span class="_ _6"> </span><span class="ff2 sc1">BP<span class="_"> </span></span>神经网络——非线性函数拟合</div><div class="t m0 x2 h3 y23 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">38<span class="_"> </span></span>章 基于<span class="_ _6"> </span><span class="ff2 sc1">Kohonen<span class="_"> </span></span>网络的聚类算法——网络入侵聚类</div><div class="t m0 x2 h3 y2a ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">39<span class="_"> </span></span>章 神经网络<span class="_ _6"> </span><span class="ff2 sc1">GUI<span class="_"> </span></span>的实现——基于<span class="_ _6"> </span><span class="ff2 sc1">GUI<span class="_"> </span></span>的神经网络拟合、模式识别、聚类</div><div class="t m0 x2 h3 y2b ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">40<span class="_"> </span></span>章 动态神经网络时间序列预测研究——基于<span class="_ _6"> </span><span class="ff2 sc1">MA<span class="_ _0"></span>TLAB<span class="_"> </span><span class="ff1 sc0">的<span class="_ _6"> </span></span>NARX<span class="_"> </span><span class="ff1 sc0">实现</span></span></div><div class="t m0 x2 h3 y2c ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _6"> </span><span class="ff2 sc1">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>
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《MATLAB 神经网络43个案例分析》源代码&数据.zip
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神经网络的经典案例分析的源代码与例题讲解
《MATLAB 神经网络43个案例分析》源代码&数据.zip
《MATLAB 神经网络43个案例分析》源代码&数据- chapter41.rar219.6KB
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