?? 多元非線性回歸分析 方法原理說明.htm
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<p class=MsoNormal align=center style='text-align:center'><b><span
style='font-size:16.0pt;mso-bidi-font-size:12.0pt;font-family:宋體;mso-ascii-font-family:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>多元非線性回歸分析</span></b><b><span
style='font-size:16.0pt;mso-bidi-font-size:12.0pt'> </span></b><b><span
style='font-size:16.0pt;mso-bidi-font-size:12.0pt;font-family:宋體;mso-ascii-font-family:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>方法原理說明</span></b><b><span
lang=EN-US style='font-size:16.0pt;mso-bidi-font-size:12.0pt'><o:p></o:p></span></b></p>
<p class=MsoBodyTextIndent>現在的非線性回歸方法種類很多，本程序主要采用的方法和原理說明如下：</p>
<p class=MsoNormal style='margin-left:46.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo2;
tab-stops:list 46.0pt'><![if !supportLists]><span lang=EN-US style='font-size:
14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'>1.<span style='font:7.0pt "Times New Roman"'>
</span></span><![endif]><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt;
font-family:宋體'>隨機爬山法<span lang=EN-US>:<o:p></o:p></span></span></p>
<p class=MsoNormal style='margin-left:28.0pt;text-indent:-28.0pt;mso-char-indent-count:
-2.0;mso-char-indent-size:14.0pt;mso-char-indent-size:14pt'><span lang=EN-US
style='font-size:14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'><span
style="mso-spacerun: yes"> </span><span style="mso-spacerun:
yes"> </span>程序不斷的按參數的取值范圍隨機產生一組組的參數，然后按程序使用者設定的回歸模型計算，找出其中和數據符合最好的一組參數，接著按當前最優參數設定新的縮小的參數取值范圍，不斷重復這個過程直到參數取值范圍足夠小，這樣就確定了參數的值。<o:p></o:p></span></p>
<p class=MsoNormal style='margin-left:46.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo2;
tab-stops:list 46.0pt'><![if !supportLists]><span lang=EN-US style='font-size:
14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'>2.<span style='font:7.0pt "Times New Roman"'>
</span></span><![endif]><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt;
font-family:宋體'>網格爬山法<span lang=EN-US>:<o:p></o:p></span></span></p>
<p class=MsoBodyTextIndent2>這和“隨機爬山法”不同的地方是程序先把參數取值區域按參數精度分割成許多塊（就像打網格），從每一塊區域的中心產生一組參數，這樣參數值的分布比較平均，其他地方與“隨機爬山法”相同。</p>
<p class=MsoNormal style='margin-left:46.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo2;
tab-stops:list 46.0pt'><![if !supportLists]><span lang=EN-US style='font-size:
14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'>3.<span style='font:7.0pt "Times New Roman"'>
</span></span><![endif]><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt;
font-family:宋體'>最速下降法<span lang=EN-US>:<o:p></o:p></span></span></p>
<p class=MsoNormal style='margin-left:28.0pt;text-indent:27.0pt;mso-char-indent-size:
14pt'><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'>隨機選定一組參數初值<span
lang=EN-US>,按給定的模型函數可以找出每個參數的最佳前進方向,如果前進方向與上一次前進方向一致就加大前進步長,如果前進方向不一致就縮小前進步長,這樣不斷的迭代就可以找到最佳參數值(有可能是局部最佳值)。程序在找出一個最佳值后還會繼續運行，重新隨機選定參數初值進行計算。<o:p></o:p></span></span></p>
<p class=MsoNormal style='margin-left:46.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo2;
tab-stops:list 46.0pt'><![if !supportLists]><span lang=EN-US style='font-size:
14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'>4.<span style='font:7.0pt "Times New Roman"'>
</span></span><![endif]><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt;
font-family:宋體'>最速下降網格爬山法<span lang=EN-US>:<o:p></o:p></span></span></p>
<p class=MsoNormal style='margin-left:28.0pt;text-indent:27.0pt'><span
style='font-size:14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'>最速下降網格爬山法是最速下降法和網格爬山法的混合使用，就是在每塊小區域中使用最速下降法找出局部最優值。<span
lang=EN-US><o:p></o:p></span></span></p>
<p class=MsoNormal style='margin-left:46.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo2;
tab-stops:list 46.0pt'><![if !supportLists]><span lang=EN-US style='font-size:
14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'>5.<span style='font:7.0pt "Times New Roman"'>
</span></span><![endif]><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt;
font-family:宋體'>基因算法<span lang=EN-US>:<o:p></o:p></span></span></p>
<p class=MsoNormal style='margin-left:28.0pt;text-indent:27.0pt'><span
style='font-size:14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'>基因算法（統稱為進化計算）是通過模擬自然界的生物進化過程來解決實際問題的一種方法，它是一種通用的問題求解方法。程序采用一種編碼技術（程序中采用的是浮點表示方案）來表示問題的解，并將每個編碼看作一個個體，算法維持一個一定數目的編碼集合，稱為種群，并通過對種群中的每個個體進行某些遺傳操作來模擬進化過程，最終獲得一些具有較高性能指標的編碼。程序把算法中的編碼稱為基因，基因的表現型就是問題的解。<span
lang=EN-US><o:p></o:p></span></span></p>
<p class=MsoNormal style='margin-left:28.0pt;text-indent:27.0pt'><span
style='font-size:14.0pt;mso-bidi-font-size:12.0pt;font-family:宋體'>進化是由四個操作組成的連續過程：繁殖、變異（基因變異、交叉）、競爭和選擇；程序中采用的遺傳算子主要有：一致變異、非一致變異、單點一致交叉、啟發式交叉、一般算術交叉和完全算術交叉。<span
lang=EN-US><o:p></o:p></span></span></p>
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12.0pt;font-family:宋體'><![if !supportEmptyParas]> <![endif]><o:p></o:p></span></p>
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12.0pt;font-family:宋體'><![if !supportEmptyParas]> <![endif]><o:p></o:p></span></p>
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12.0pt'><![if !supportEmptyParas]> <![endif]><o:p></o:p></span></p>
<p class=MsoNormal align=center style='text-align:center'><span lang=EN-US
style='font-size:9.0pt;mso-bidi-font-size:12.0pt'><a href="多元非線性回歸分析%20幫助主題.htm"><span
style='font-family:宋體;mso-ascii-font-family:"Times New Roman";mso-hansi-font-family:
"Times New Roman"'>幫助主題</span></a><o:p></o:p></span></p>
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