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目录
💥1 概述
📚2 运行结果
🎉3 参考文献
🌈4 Matlab代码实现
💥1 概述
本文使用 PSO 算法更新隐藏层和输出层的权重和偏差。
📚2 运行结果
部分代码：
function NMSE_calc = NMSE( wb, net, input, target)
% wb is the weights and biases row vector obtained from the genetic algorithm.
% It must be transposed when transferring the weights and biases to the network net.
net = setwb(net, wb');
% The net output matrix is given by net(input). The corresponding error matrix is given by
error = target - net(input);
% The mean squared error normalized by the mean target variance is
NMSE_calc = mean(error.^2)/mean(var(target',1));
% It is independent of the scale of the target components and related to the Rsquare statistic via
% Rsquare = 1 - NMSEcalc ( see Wikipedia)
🎉3 参考文献
[1]Selva (2022). particle swarm optimized Neural Network.
