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Description: Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms - Industrial Applications-Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms - Industrial Applications::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
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Size: 3138560 |
Author: Qasim |
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Description: 为了更好了解遗传神经网络在系统中的对比效果，我们以室内温度控制为例，设室内的温度控制目标为18℃（20℃），其他参数保持不变的情况下，分别按照单独神经网络和基于遗传算法优化后的神经网络控制进行仿真实验.仿真结果表明，上述应用遗传算法优化的神经网络是非常有效的，通过运用遗传算法对神经网络进行优化，使其具有良好的泛化能力和快速的收敛性。-To better understand the genetic neural network contrast in the system, we control the indoor temperature, for example, set the room temperature control goal is 18 ℃ (20 ℃), other parameters remaining unchanged, respectively, according to a separate neural networks optimized based on genetic algorithm and neural network control simulation. The simulation results show that the genetic algorithm to optimize the neural network is very effective, through the use of genetic algorithms to optimize the neural network, it has a good generalization ability and fast convergence.
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Size: 19456 |
Author: |
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Description: 基于遗传算法的BP神经网络在多目标优化中的应用研究-BP based on genetic algorithm neural network in multi-objective optimization of
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Size: 2745344 |
Author: lili |
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Description: 主要是遗传神经网络运用于工业实践，欢迎大家参加借鉴，也欢迎交流与合作。-Genetic neural network is mainly used in industrial practice, welcome you to learn, exchange and cooperation are also welcome.
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Size: 4096 |
Author: zhoumin |
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Description: 运用matlab工具箱的神经网络遗传算法函数进行极值寻优-非线性函数极值-Matlab neural network using genetic algorithm toolbox function optimization Extreme- Extreme nonlinear function
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Size: 102400 |
Author: Jv |
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Description: 神经网络算法的matlab源程序及详细解说，源码已经过验证-Neural network algorithm matlab source code and detailed explanations, source code has been verified
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Size: 338944 |
Author: 李明 |
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Description: The use of genetic algorithms to train neural networks
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Size: 1024 |
Author: genei |
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Description: 遗传算法\人工神经网络讲稿，对遗传算法惊醒了详细的介绍。-The genetic algorithm, the artificial neural network notes
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Size: 139264 |
Author: 唐秀君 |
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Description: GA to RBF 神经网络，遗传算法，滤波器，MATLAB-GA to RBF neural networks, genetic algorithms, filters, MATLAB
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Size: 3072 |
Author: shdfk |
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Description: Through neural networks, ant colony algorithm, genetic algorithm, such as intelligent optimization methods to solve TSP problems
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Size: 32768 |
Author: asiatak |
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Description: 神经网络概述及基本模型，然后详述了各种模型，包括感知器神经模型、自组织竞争神经网络、径向基函数神经网络、反馈神经网络、支持向量机神经网络、遗传算法等内容。-Overview and basic neural network model, and then details the various models, including perceptron neural model, self-organizing competitive neural network, radial basis function neural network, the feedback neural networks, support vector machine neural networks, genetic algorithms and so on.
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Size: 10161152 |
Author: 王龙 |
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Description: 本程序使用遗传算法优化BP神经网络来进行非线性函数拟合-This program uses genetic algorithms to optimize the BP neural network for nonlinear function fitting
Platform: |
Size: 52224 |
Author: 费斌 |
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Description: 遗传算法优化的BP神经网络算法，利用遗传算法优化BP神经网络的权值和阈值-Genetic algorithm BP neural network
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Size: 2048 |
Author: www000 |
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Description: 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.
Platform: |
Size: 217088 |
Author: heddam salim |
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Description: 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.
Platform: |
Size: 186368 |
Author: heddam salim |
Hits:
Description: 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.
Platform: |
Size: 214016 |
Author: heddam salim |
Hits:
Description: 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.
Platform: |
Size: 183296 |
Author: heddam salim |
Hits:
Description: 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.
Platform: |
Size: 209920 |
Author: heddam salim |
Hits:
Description: 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.
Platform: |
Size: 217088 |
Author: heddam salim |
Hits:
Description: 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.
Platform: |
Size: 184320 |
Author: heddam salim |
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