<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Liang_1_的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Liang_1_</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Liang_1_]]></copyright><item><title><![CDATA[爱心代码实现]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/131284965</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/131284965</guid><author>Liang_1_</author><pubDate>Mon, 19 Jun 2023 12:11:02 +0800</pubDate><description><![CDATA[i <= 5;i++) {j < 5 - i;j++) {j < i;j++) {j++) {j < i;j++) {i <= 10;i++) {j < i;j++) {j < 10 - i;j++) {]]></description><category></category></item><item><title><![CDATA[Java心型代码]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/131284958</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/131284958</guid><author>Liang_1_</author><pubDate>Mon, 19 Jun 2023 12:10:37 +0800</pubDate><description><![CDATA[i <= 5;i++) {j < 5 - i;j++) {j < i;j++) {j++) {j < i;j++) {i <= 10;i++) {j < i;j++) {j < 10 - i;j++) {]]></description><category></category></item><item><title><![CDATA[贝叶斯算法基本原理]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130334396</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130334396</guid><author>Liang_1_</author><pubDate>Mon, 24 Apr 2023 08:06:54 +0800</pubDate><description><![CDATA[Yes:   return xold,target(xold)    #撞了大运，还没开始迭代就结束了。利用PF(高斯过程回归)求解未知点(在事先定义的自变量范围内，有成千上万个未知点)的均值与方差。利用AC(EI或PI或UCB)找到贝叶斯优化器猜测的最大值的点xnew，一般是AC函数最大点。xold为已知的所有点中目标函数取得最大值的自变量值。No:  随机初始化。]]></description><category></category></item><item><title><![CDATA[贝叶斯优化算法]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130334394</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130334394</guid><author>Liang_1_</author><pubDate>Mon, 24 Apr 2023 08:06:20 +0800</pubDate><description><![CDATA[Yes:   return xold,target(xold)    #撞了大运，还没开始迭代就结束了。利用PF(高斯过程回归)求解未知点(在事先定义的自变量范围内，有成千上万个未知点)的均值与方差。利用AC(EI或PI或UCB)找到贝叶斯优化器猜测的最大值的点xnew，一般是AC函数最大点。xold为已知的所有点中目标函数取得最大值的自变量值。No:  随机初始化。]]></description><category></category></item><item><title><![CDATA[转换视频原理基本]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130262349</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130262349</guid><author>Liang_1_</author><pubDate>Thu, 20 Apr 2023 11:18:31 +0800</pubDate><description><![CDATA[Codecs:-------]]></description><category></category></item><item><title><![CDATA[转换视频编码]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130262334</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130262334</guid><author>Liang_1_</author><pubDate>Thu, 20 Apr 2023 11:17:59 +0800</pubDate><description><![CDATA[Codecs:-------]]></description><category></category></item><item><title><![CDATA[最小二乘法程序]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130218733</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130218733</guid><author>Liang_1_</author><pubDate>Tue, 18 Apr 2023 12:00:25 +0800</pubDate><description><![CDATA[phi(x)为基函数,通常为多项式1,x,x^2,...%n为数据拟合的次数，缺省时n=1;%x,y为数据的(x,y)坐标;%数据的最小二乘拟合,其中。%w为权值，缺省值w=1;%S为数据拟合的系数;%需要另写的外部函数.]]></description><category></category></item><item><title><![CDATA[最小二乘法程序]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130218722</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130218722</guid><author>Liang_1_</author><pubDate>Tue, 18 Apr 2023 11:59:44 +0800</pubDate><description><![CDATA[phi(x)为基函数,通常为多项式1,x,x^2,...%n为数据拟合的次数，缺省时n=1;%x,y为数据的(x,y)坐标;%数据的最小二乘拟合,其中。%w为权值，缺省值w=1;%S为数据拟合的系数;%需要另写的外部函数.]]></description><category></category></item><item><title><![CDATA[鲸鱼算法原理]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130193116</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130193116</guid><author>Liang_1_</author><pubDate>Mon, 17 Apr 2023 09:39:51 +0800</pubDate><description><![CDATA[参数初始化，初始时主要设置代理数量和最大迭代次数即可，其他算法相关的参数因为和当前迭代次数相关，需要在迭代中设置。]]></description><category></category></item><item><title><![CDATA[鲸鱼算法程序]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130193096</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130193096</guid><author>Liang_1_</author><pubDate>Mon, 17 Apr 2023 09:39:11 +0800</pubDate><description><![CDATA[参数初始化，初始时主要设置代理数量和最大迭代次数即可，其他算法相关的参数因为和当前迭代次数相关，需要在迭代中设置。]]></description><category></category></item><item><title><![CDATA[深度学习原理]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130180301</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130180301</guid><author>Liang_1_</author><pubDate>Sun, 16 Apr 2023 11:07:57 +0800</pubDate><description><![CDATA[print("使用纯Python用时{}s".format(time.time()-start))print("使用Numpy用时{}s".format(time.time()-start))]]></description><category></category></item><item><title><![CDATA[深度学习程序]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130180293</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130180293</guid><author>Liang_1_</author><pubDate>Sun, 16 Apr 2023 11:07:26 +0800</pubDate><description><![CDATA[print("使用纯Python用时{}s".format(time.time()-start))print("使用Numpy用时{}s".format(time.time()-start))]]></description><category></category></item><item><title><![CDATA[深度学习程序原理基本]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130168313</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130168313</guid><author>Liang_1_</author><pubDate>Sat, 15 Apr 2023 12:19:44 +0800</pubDate><description><![CDATA[print("使用纯Python用时{}s".format(time.time()-start))print("使用Numpy用时{}s".format(time.time()-start))]]></description><category></category></item><item><title><![CDATA[深度学习程序]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130168308</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130168308</guid><author>Liang_1_</author><pubDate>Sat, 15 Apr 2023 12:19:07 +0800</pubDate><description><![CDATA[print("使用纯Python用时{}s".format(time.time()-start))print("使用Numpy用时{}s".format(time.time()-start))]]></description><category></category></item><item><title><![CDATA[Emd能量分解]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130151957</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130151957</guid><author>Liang_1_</author><pubDate>Fri, 14 Apr 2023 13:50:39 +0800</pubDate><description><![CDATA[11 # 绘制并显示所有提供的IMF的瞬时频率。]]></description><category></category></item><item><title><![CDATA[经验模态能量提取]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130151945</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130151945</guid><author>Liang_1_</author><pubDate>Fri, 14 Apr 2023 13:50:03 +0800</pubDate><description><![CDATA[11 # 绘制并显示所有提供的IMF的瞬时频率。]]></description><category></category></item><item><title><![CDATA[灰狼程序优化]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130122952</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130122952</guid><author>Liang_1_</author><pubDate>Thu, 13 Apr 2023 09:45:47 +0800</pubDate><description><![CDATA[适应度函数表达式(求这个函数的最大值)fplot(f, [0 20], 'b-');% 更新 Alpha, Beta, and Delta。% 初始化alpha,beta,delta。]]></description><category></category></item><item><title><![CDATA[灰狼算法程序]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130122934</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130122934</guid><author>Liang_1_</author><pubDate>Thu, 13 Apr 2023 09:45:16 +0800</pubDate><description><![CDATA[适应度函数表达式(求这个函数的最大值)fplot(f, [0 20], 'b-');% 更新 Alpha, Beta, and Delta。% 初始化alpha,beta,delta。]]></description><category></category></item><item><title><![CDATA[滑动窗口算法]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130099829</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130099829</guid><author>Liang_1_</author><pubDate>Wed, 12 Apr 2023 09:51:36 +0800</pubDate><description><![CDATA[/ 这里的list的首位必须是窗口中最大的那位。// 构造窗口完成，这时候需要根据条件做一些操作。// 如果发现第一个已经在窗口外面了，就移除。]]></description><category></category></item><item><title><![CDATA[滑动平均算法]]></title><link>https://blog.youkuaiyun.com/Liang_1_/article/details/130099846</link><guid>https://blog.youkuaiyun.com/Liang_1_/article/details/130099846</guid><author>Liang_1_</author><pubDate>Wed, 12 Apr 2023 09:49:24 +0800</pubDate><description><![CDATA[/ 这里的list的首位必须是窗口中最大的那位。// 构造窗口完成，这时候需要根据条件做一些操作。// 如果发现第一个已经在窗口外面了，就移除。]]></description><category></category></item></channel></rss>