MATLAB智能算法30个案例源代码
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案例分析源代码/案例分析源代码/chapter1.rar
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案例分析源代码/案例分析源代码/《MATLAB 神经网络43个案例分析》目录.docx
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资源内容介绍
《MATLAB智能算法30个案例分析》采用案例形式,以智能算法为主线,讲解了遗传算法、免疫算法、退火算法、粒子群算法、鱼群算法、蚁群算法和神经网络算法等Z常用的智能算法的MATLAB实现.本书共给出３０个案例,每个案例都是一个使用智能算法解决问题的具体实例,所有案例均由理论讲解、案例背景、MATLAB程序实现和扩展阅读四个部分组成。 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89604374/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89604374/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">《<span class="ff2 sc1">MATLAB <span class="_ _0"> </span></span>神经网络<span class="_ _0"> </span><span class="ff2 sc1">43<span class="_"> </span></span>个案例<span class="_ _1"></span>分析》目录</div><div class="t m0 x2 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _0"> </span><span class="ff2 sc1">1<span class="_"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>BP<span class="_ _2"> </span></span>神经网络的数据<span class="_ _1"></span>分类<span class="ff3">——</span>语音<span class="_ _1"></span>特征信号分类</div><div class="t m0 x2 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">2<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>BP<span class="_ _2"> </span></span>神经网络的非线<span class="_ _1"></span>性系统建模<span class="ff3">—<span class="_ _1"></span>—</span>非线性函数<span class="_ _1"></span>拟合</div><div class="t m0 x2 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">3<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>遗传算法优化<span class="_ _2"> </span><span class="ff2 sc1">BP<span class="_ _2"> </span></span>神<span class="_ _1"></span>经网络<span class="ff3">——</span>非<span class="_ _1"></span>线性函数拟合</div><div class="t m0 x2 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">4<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>神经网络遗传<span class="_ _1"></span>算法函数极值寻<span class="_ _1"></span>优<span class="ff3">——</span>非线性<span class="_ _1"></span>函数极值寻优</div><div class="t m0 x2 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">5<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于<span class="_ _2"> </span><span class="ff2 sc1">BP_Adaboost<span class="_ _2"> </span></span>的强分类器设<span class="_ _1"></span>计<span class="ff3">——</span>公司财<span class="_ _1"></span>务预警建模</div><div class="t m0 x2 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">6<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>PID<span class="_ _2"> </span></span>神经元网络解耦<span class="_ _1"></span>控制算法<span class="ff3">——<span class="_ _1"></span></span>多变量系统控<span class="_ _1"></span>制</div><div class="t m0 x2 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">7<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>RBF<span class="_ _2"> </span></span>网络的回归<span class="ff2 sc1">--</span>非线<span class="_ _1"></span>性函数回归的<span class="_ _1"></span>实现</div><div class="t m0 x2 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">8<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>GRNN<span class="_ _2"> </span></span>网络的预测<span class="ff2 sc1">----</span>基于<span class="_ _1"></span>广义回归神经<span class="_ _1"></span>网络的货运量<span class="_ _1"></span>预测</div><div class="t m0 x2 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">9<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>离散<span class="_ _2"> </span><span class="ff2 sc1">Hopfield<span class="_ _2"> </span></span>神经网络的联<span class="_ _1"></span>想记忆<span class="ff3">——</span>数<span class="_ _1"></span>字识别</div><div class="t m0 x2 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">10<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>离散<span class="_ _2"> </span><span class="ff2 sc1">Hopfield<span class="_ _2"> </span></span>神经网络的分<span class="_ _1"></span>类<span class="ff3">——</span>高校科<span class="_ _1"></span>研能力评价</div><div class="t m0 x2 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">11<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>连续<span class="_ _2"> </span><span class="ff2 sc1">Hopfield<span class="_ _2"> </span></span>神经网络的优<span class="_ _1"></span>化<span class="ff3">——</span>旅行商<span class="_ _1"></span>问题优化计算</div><div class="t m0 x2 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">12<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>初始<span class="_ _2"> </span><span class="ff2 sc1">SVM<span class="_ _2"> </span></span>分类与回归</div><div class="t m0 x2 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">13<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>LIBSVM<span class="_ _2"> </span></span>参数实例详解</div><div class="t m0 x2 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">14<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于<span class="_ _2"> </span><span class="ff2 sc1">SVM<span class="_ _2"> </span></span>的数据分类<span class="_ _1"></span>预测<span class="ff3">——</span>意大<span class="_ _1"></span>利葡萄酒种类识<span class="_ _1"></span>别</div><div class="t m0 x2 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">15<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>SVM<span class="_ _2"> </span></span>的参数优化<span class="ff3">——<span class="_ _1"></span></span>如何更好的提<span class="_ _1"></span>升分类器的性<span class="_ _1"></span>能</div><div class="t m0 x2 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">16<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于<span class="_ _2"> </span><span class="ff2 sc1">SVM<span class="_ _2"> </span></span>的回归预测<span class="_ _1"></span>分析<span class="ff3">——</span>上证<span class="_ _1"></span>指数开盘指数预<span class="_ _1"></span>测<span class="ff2 sc1">.</span></div><div class="t m0 x2 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">17<span class="_"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于<span class="_ _2"> </span><span class="ff2 sc1">SVM<span class="_"> </span></span>的信<span class="_ _1"></span>息粒化时序回<span class="_ _1"></span>归预测<span class="ff3">——</span>上证<span class="_ _1"></span>指数开盘指数<span class="_ _1"></span>变化趋势和变<span class="_ _1"></span>化空间预</div><div class="t m0 x2 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">测</div><div class="t m0 x2 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">18<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于<span class="_ _2"> </span><span class="ff2 sc1">SVM<span class="_ _2"> </span></span>的图像分割<span class="_ _1"></span><span class="ff2 sc1">-</span>真彩色图像分割</div><div class="t m0 x2 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">19<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于<span class="_ _2"> </span><span class="ff2 sc1">SVM<span class="_ _2"> </span></span>的手写字体<span class="_ _1"></span>识别</div><div class="t m0 x2 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">20<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>LIBSVM-FarutoUltimate<span class="_"> </span></span>工<span class="_ _1"></span>具箱及<span class="_ _2"> </span><span class="ff2 sc1">GUI<span class="_ _2"> </span></span>版本介绍<span class="_ _1"></span>与使用</div><div class="t m0 x2 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">21<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>自组织竞争网<span class="_ _1"></span>络在模式分类中<span class="_ _1"></span>的应用<span class="ff3">—</span>患者<span class="_ _1"></span>癌症发病预测</div><div class="t m0 x2 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">22<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>SOM<span class="_ _2"> </span></span>神经网络的数据<span class="_ _1"></span>分类<span class="ff2 sc1">--</span>柴油机故<span class="_ _1"></span>障诊断</div><div class="t m0 x2 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">23<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>Elman<span class="_ _2"> </span></span>神经网络的数据<span class="_ _1"></span>预测<span class="ff2 sc1">----</span>电力负荷<span class="_ _1"></span>预测模型研究</div><div class="t m0 x2 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">24<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>概率神经网络<span class="_ _1"></span>的分类预测<span class="ff2 sc1">--</span>基<span class="_ _1"></span>于<span class="_ _2"> </span><span class="ff2 sc1">PNN<span class="_ _2"> </span></span>的变压器故障诊<span class="_ _1"></span>断</div><div class="t m0 x2 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">25<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于<span class="_ _2"> </span><span class="ff2 sc1">MIV<span class="_ _2"> </span></span>的神经网络<span class="_ _1"></span>变量筛选<span class="ff2 sc1">----</span>基于<span class="_ _2"> </span><span class="ff2 sc1">BP<span class="_ _2"> </span></span>神<span class="_ _1"></span>经网络的变量筛<span class="_ _1"></span>选</div><div class="t m0 x2 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">26<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>LVQ<span class="_ _2"> </span></span>神经网络的分类<span class="_ _1"></span><span class="ff3">——</span>乳腺肿瘤<span class="_ _1"></span>诊断</div><div class="t m0 x2 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">27<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span>LVQ<span class="_ _2"> </span></span>神经网络的预测<span class="_ _1"></span><span class="ff3">——</span>人脸朝向<span class="_ _1"></span>识别</div><div class="t m0 x2 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">28<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>决策树分类器<span class="_ _1"></span>的应用研究<span class="ff3">—<span class="_ _1"></span>—</span>乳腺癌诊断</div><div class="t m0 x2 h2 y1f ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">29<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>极限学习机在<span class="_ _1"></span>回归拟合及分类<span class="_ _1"></span>问题中的应用<span class="_ _1"></span>研究<span class="ff3">——</span>对比<span class="_ _1"></span>实验</div><div class="t m0 x2 h2 y20 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">30<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于随机森林<span class="_ _1"></span>思想的组合分类<span class="_ _1"></span>器设计<span class="ff3">——</span>乳<span class="_ _1"></span>腺癌诊断</div><div class="t m0 x2 h2 y21 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">31<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>思维进化算法<span class="_ _1"></span>优化<span class="_ _2"> </span><span class="ff2 sc1">BP<span class="_ _2"> </span></span>神经网络<span class="ff3">——<span class="_ _1"></span></span>非线性函数拟<span class="_ _1"></span>合</div><div class="t m0 x2 h2 y22 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">32<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>小波神经网络<span class="_ _1"></span>的时间序列预测<span class="_ _1"></span><span class="ff3">——</span>短时交通<span class="_ _1"></span>流量预测</div><div class="t m0 x2 h2 y23 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">33<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>模糊神经网络<span class="_ _1"></span>的预测算法<span class="ff3">—<span class="_ _1"></span>—</span>嘉陵江水质评<span class="_ _1"></span>价</div><div class="t m0 x2 h2 y24 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">34<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>广义神经网络<span class="_ _1"></span>的聚类算法<span class="ff3">—<span class="_ _1"></span>—</span>网络入侵聚类</div><div class="t m0 x2 h2 y25 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">35<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>粒子群优化算<span class="_ _1"></span>法的寻优算法<span class="_ _1"></span><span class="ff3">——</span>非线性函数<span class="_ _1"></span>极值寻优</div><div class="t m0 x2 h2 y26 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">36<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>遗传算法优化<span class="_ _1"></span>计算<span class="ff3">——</span>建模<span class="_ _1"></span>自变量降维</div><div class="t m0 x2 h2 y27 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">37<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于灰色神经<span class="_ _1"></span>网络的预测算法<span class="_ _1"></span>研究<span class="ff3">——</span>订单<span class="_ _1"></span>需求预测</div><div class="t m0 x2 h2 y28 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">38<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>基于<span class="_ _2"> </span><span class="ff2 sc1">Kohonen<span class="_ _2"> </span></span>网络的聚类算<span class="_ _1"></span>法<span class="ff3">——</span>网络入<span class="_ _1"></span>侵聚类</div><div class="t m0 x2 h2 y29 ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">39<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>神经网络<span class="_ _2"> </span><span class="ff2 sc1">GUI<span class="_ _2"> </span></span>的实现<span class="_ _1"></span><span class="ff3">——</span>基于<span class="_ _2"> </span><span class="ff2 sc1">GUI<span class="_ _2"> </span></span>的神经网<span class="_ _1"></span>络拟合、模式<span class="_ _1"></span>识别、聚类</div><div class="t m0 x2 h2 y2a ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">40<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>动态神经网络<span class="_ _1"></span>时间序列预测研<span class="_ _1"></span>究<span class="ff3">——</span>基于<span class="_ _2"> </span><span class="ff2 sc1">MATLAB<span class="_ _2"> </span></span>的<span class="_ _2"> </span><span class="ff2 sc1">NARX<span class="_ _2"> </span></span>实现</div><div class="t m0 x2 h2 y2b ff1 fs0 fc0 sc0 ls0 ws0">第<span class="_ _2"> </span><span class="ff2 sc1">41<span class="_ _2"> </span></span>章<span class="ff2 sc1"> <span class="_ _0"> </span></span>定制神经网络<span class="_ _1"></span>的实现<span class="ff3">——</span>神<span class="_ _1"></span>经网络的个性化<span class="_ _1"></span>建模与仿真</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>