【PSO：利用PSO实现对一元函数y = sin(10*pi*x) ./ x进行求解优化，找到最优个体适应度—Jason niu】的更多相关文章

PSO：利用PSO+ω参数实现对一元函数y = sin(10*pi*x) ./ x进行求解优化，找到最优个体适应度—Jason niu

x = 1:0.01:2; y = sin(10*pi*x) ./ x; figure plot(x, y) title('绘制目标函数曲线图—Jason niu'); hold on c1 = 1.49445; c2 = 1.49445; maxgen = 50; sizepop = 10; Vmax = 0.5; Vmin = -0.5; popmax = 2; popmin = 1; ws = 0.9; we = 0.4; for i = 1:sizepop pop(i,:) = (ran…

PSO：利用PSO实现对一元函数y = sin(10*pi*x) ./ x进行求解优化，找到最优个体适应度—Jason niu

x = 1:0.01:2; y = sin(10*pi*x) ./ x; figure plot(x, y) title('绘制目标函数曲线图—Jason niu'); hold on c1 = 1.49445; c2 = 1.49445; maxgen = 50; sizepop = 10; Vmax = 0.5; Vmin = -0.5; popmax = 2; popmin = 1; for i = 1:sizepop pop(i,:) = (rands(1) + 1) / 2 + 1;…

PSO：利用PSO算法优化二元函数，寻找最优个体适应度—Jason niu

figure [x,y] = meshgrid(-5:0.1:5,-5:0.1:5); z = x.^2 + y.^2 - 10*cos(2*pi*x) - 10*cos(2*pi*y) + 20; mesh(x,y,z) hold on c1 = 1.49445; c2 = 1.49445; maxgen = 1000; sizepop = 100; Vmax = 1; Vmin = -1; popmax = 5; popmin = -5; for i = 1:sizepop pop(i,:)…

TF：利用TF的train.Saver载入曾经训练好的variables(W、b)以供预测新的数据—Jason niu

import tensorflow as tf import numpy as np W = tf.Variable(np.arange(6).reshape((2, 3)), dtype=tf.float32, name="weights") b = tf.Variable(np.arange(3).reshape((1, 3)), dtype=tf.float32, name="biases") saver = tf.train.Saver() with tf.…

GA：利用GA对一元函数进行优化过程，求x∈(0,10)中y的最大值——Jason niu

x = 0:0.01:10; y = x + 10*sin(5*x)+7*cos(4*x); figure plot(x, y) xlabel('independent variable') ylabel('dependent variable') title('GA:y = x + 10*sin(5*x) + 7*cos(4*x)利用算法求解最优解—Jason niu') initPop = initializega(50,[0 10],'fitness'); [x endPop bpop t…

[再寄小读者之数学篇](2014-11-19 $\sin(x+y)=\sin x\cos y+\cos x\sin y$)

$$\bex \sin(x+y)=\sin x\cos y+\cos x\sin y. \eex$$ Ref. [Proof Without Words: Sine Sum Identity, The College Mathematics Journal].…

函数y=sin(1/x)曲线

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鸟枪换炮，利用python3对球员做大数据降维(因子分析得分)，为C罗找到合格僚机

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load spectra; temp = randperm(size(NIR, 1)); P_train = NIR(temp(1:50),:); T_train = octane(temp(1:50),:); P_test = NIR(temp(51:end),:); T_test = octane(temp(51:end),:); k = 2; [Xloadings,Yloadings,Xscores,Yscores,betaPLS,PLSPctVar,MSE,stats] = plsreg…

PCA：利用PCA(四个主成分的贡献率就才达100%)降维提高测试集辛烷值含量预测准确度并《测试集辛烷值含量预测结果对比》—Jason niu

load spectra; temp = randperm(size(NIR, 1)); P_train = NIR(temp(1:50),:); T_train = octane(temp(1:50),:); P_test = NIR(temp(51:end),:); T_test = octane(temp(51:end),:); [PCALoadings,PCAScores,PCAVar] = princomp(NIR); figure percent_explained = 100 *…