上传列表：

N-GEN-(5).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:26:43,下载2次
N-GEN-(6).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:24:47,下载2次
N-GEN-(7).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:23:37,下载3次
N-GEN-(8).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:22:08,下载2次
N-GEN-(9).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:20:42,下载2次
N-GEN-(10).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:19:14,下载2次
N-GEN-(11).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:17:39,下载3次
N-GEN-(12).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:15:48,下载2次
N-GEN-(13).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:14:13,下载2次
N-GEN-(17).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-07 00:06:29,下载2次
N-GEN-(19).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-06 23:36:02,下载6次
N-GEN-(20).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-06 23:34:06,下载2次
N-GEN-(21).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-06 23:31:02,下载2次
N-GEN-(22).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-06 23:29:02,下载1次
N-GEN-(23).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2012-01-06 23:25:56,下载2次
NEURO-GENETIC.zip-(8).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 04:32:14,下载3次
NEURO-GENETIC.zip-(9).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 04:28:31,下载5次
NEURO-GENETIC.zip-(10).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 04:24:36,下载2次
NEURO-GENETIC.zip-(11).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 04:08:11,下载5次
NEURO-GENETIC.zip-(12).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 04:03:55,下载4次
NEURO-GENETIC.zip-(13).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 03:56:35,下载2次
NEURO-GENETIC.zip-(14).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 03:52:59,下载4次
NEURO-GENETIC.zip-(15).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 03:42:06,下载2次
NEURO-GENETIC.zip-(5).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 01:51:44,下载5次
NEURO-GENETIC.zip-(4).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 01:49:34,下载3次
NEURO-GENETIC.zip-(3).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 01:45:37,下载2次
NEURO-GENETIC.zip-(2).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 01:36:09,下载2次
NEURO-GENETIC.zip-(1).zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 01:28:37,下载2次
NEURO-GENETIC.zip - The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.,2011-12-07 01:26:37,下载2次
neuro-fuzzy.rar - MATLAB SOFTWARE TOOL FOR NEURO-FUZZY IDENTIFICATION AND DATA ANALYSIS. ,2011-12-06 05:06:04,下载11次
RECURRENT-ANN-(17).rar - RECURRENT NEURAL NETWORK ,2011-12-05 21:16:30,下载2次
RECURRENT-ANN-(16).rar - RECCURENT NEURAL NETWORK RNN,2011-12-05 21:13:45,下载15次
RECURRENT-ANN-(15).rar - RECURRENT NEURAL NETWORK RNN,2011-12-05 21:08:08,下载5次
RECURRENT-ANN-(14).rar - Recurrent Neural Networks (RNNs),2011-12-05 21:02:49,下载4次
RECURRENT-ANN-(13).rar - recurrent neural network (RNN),2011-12-05 20:58:46,下载6次
RECURRENT-ANN-(12).rar - recurrent neural network (RNN),2011-12-05 20:56:23,下载9次
RECURRENT-ANN-(11).rar - recurrent neural network (RNN),2011-12-05 20:54:06,下载9次
RECURRENT-ANN-(10).rar - recurrent neural network (RNN),2011-12-05 20:47:50,下载8次
RECURRENT-ANN-(9).rar - recurrent neural network (RNN),2011-12-05 20:42:45,下载7次
RECURRENT-ANN-(8).rar - recurrent neural network (RNN),2011-12-05 20:38:17,下载6次
RECURRENT-ANN-(7).rar - recurrent neural network (RNN),2011-12-05 20:33:53,下载18次
RECURRENT-ANN-(6).rar - recurrent neural network (RNN),2011-12-05 20:30:59,下载10次
RECURRENT-ANN-(5).rar - recurrent neural network (RNN),2011-12-05 20:29:09,下载22次
RECURRENT-ANN-(4).rar - recurrent neural network (RNN),2011-12-05 20:27:41,下载6次
RECURRENT-ANN-(3).rar - recurrent neural network (RNN),2011-12-05 20:25:18,下载7次
RECURRENT-ANN-(2).rar - recurrent neural network (RNN),2011-12-05 20:22:07,下载4次
RECURRENT-ANN-(1).rar - recurrent neural network (RNN),2011-12-05 20:20:07,下载28次
RECURRENT-ANN.rar - recurrent neural network (RNN),2011-12-05 20:17:22,下载20次
SA-(16).rar - THANKING YOU VERY MUCH ,2011-10-23 16:12:03,下载3次
SA-(14).rar - THANKING YOU VERY MUCH ,2011-10-23 16:08:05,下载2次
SA-(13).rar - THANKING YOU VERY MUCH ,2011-10-23 16:06:57,下载2次
SA-(12).rar - THANKING YOU VERY MUCH ,2011-10-23 16:05:36,下载2次
SA-(11).rar - THANKING YOU VERY MUCH ,2011-10-23 16:04:28,下载2次
SA-(10).rar - THANKING YOU VERY MUCH ,2011-10-23 16:01:23,下载2次
SA-(9).rar - THANKING YOU VERY MUCH ,2011-10-23 15:58:22,下载2次
SA-(8).rar - THANKING YOU VERY MUCH ,2011-10-23 15:54:55,下载2次
SA-(7).rar - THANKING YOU VERY MUCH ,2011-10-23 15:52:21,下载2次
SA-(6).rar - THANKING YOU VERY MUCH ,2011-10-23 15:51:39,下载2次
SA-(5).rar - THANKING YOU VERY MUCH ,2011-10-23 15:49:18,下载1次
SA-(4).rar - THANKING YOU VERY MUCH ,2011-10-23 15:47:41,下载2次
SA-(3).rar - THANKING YOU VERY MUCH ,2011-10-23 15:44:31,下载2次
SA-(2).rar - THANKING YOU VERY MUCH ,2011-10-23 15:40:02,下载2次
SA-(1).rar - THANKING YOU VERY MUCH ,2011-10-23 15:39:04,下载2次
fulltext(11).rar - thanking you very much,2011-10-19 16:22:12,下载3次
fulltext(10).rar - thanking you very much,2011-10-19 16:21:32,下载2次
fulltext(8).rar - thanking you very much,2011-10-19 16:20:47,下载3次
fulltext(7).rar - thanking you very much,2011-10-19 16:20:03,下载3次
fulltext(1).rar - thanking you very much ,2011-10-19 16:18:58,下载3次

近期下载：

ER-WCA.zip - ER-WCA_Constrained-Evaporation Based Water Cycle Algorithm (ERWCA) for solving constrained optimization problems
MOWCA_Unconstrained.zip - Water Cycle Algorithm for solving multi-objective optimization problems (water cycle algorith, optimization)
ERWCA_unconstrained.rar - 具有蒸发率的水循环算法，用来解决无约束优化问题，这是一个比较新的算法
WCA-Source-Codes.rar - water cycle algorithm for optimization
MFO.zip - 飞蛾火焰优化算法、原论文及matlab仿真程序
MFO.zip - 一种新颖的启发式优化算法：飞蛾扑火算法（MFO）
MFO.zip - 人工智能优化算法MFO，效果很好，推荐下载
MFo.rar - 这是一种启发式算法飞蛾火焰算法,是一种新型群智能算法
MFO.rar - Moth-flame Optimization (MFO) Algorithm is a novel meta-heuristic algorithm for solving optimization problems
bpbybat.rar - 此代码创建了一个BP神经网络，对二元函数进行拟合，并利用蝙蝠算法对BP神经网络的权值和阈值进行优化
ESNtools.zip - 回声状态神经网络(ESN)是一种性能优异的递归神经网络，已经在各领域广泛研究，这是ESN的发明人研制的MATLAB工具箱，可供有关人员参考使用
CNN-Toolbox.zip - 深度学习的Toolbox,使用mnist_uint8手写字数据库，适合初学者使用。
CNN.rar - 深度学习中卷积神经网络，实现手写数字的分类，其中包含网络的初始化，训练，测试三个模块。包含mnist_uint8.mat文件，CNN入门学习程序。
DeepLearnToolbox.zip - DeepLearnToolbox是Matlab的深度学习工具箱，包含了深度信度网络DBN，卷积网络CNN，SAE（stacked auto-encoders）,CAE(Convolutional auto-encoders)和NN深度学习算法的实现。
cnn_exercise.zip - 深度学习CNN代码,帮助理解卷积网络，代码是可以运行的，没有问题
DeepLearnToolbox-master.zip - deep learning 包含CAE CNN NN SAE和DBN算法 使用matlab工具 同时附带了 mnist_uint8.mat 测试图像
spiderplot.zip - Plots the factor loadings (yax) as a spiderplot.
原始VMD.rar - 变分模态分解算法是2014年新提出的，广泛应用于信号处理领域。 VMD分解的子程序，给定分解的时域信号，数据保真约束平衡参数、噪声波峰时间步长、VMD分解个数、迭代精度可以得到 分解的子分量、子分量的波谱、各模式的中心频率。
Variant Mode Decomposition VMD original.zip - 2014年，新的变模态分解方法被提出，优于EMD算法
mvmd.rar - 多元变分模态分解，适用多通道信号模态分解，变分模态分解的升级版

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