An optimization algorithm design based on chaotic variable is proposed for multilayer fuzzy neural network.
提出了一种基于混沌变量的多层模糊神经网络优化算法设计。
In the third part, the prediction model of fuzzy optimal selection neural network based on chaotic optimization algorithm is studied.
第三部分对基于混沌优化算法的模糊优选神经网络预测模型进行研究。
A fast stochastic global optimization algorithm, particle group optimization algorithm, was used for training the fuzzy neural network.
模糊神经网络的学习算法采用的是快速的粒子群优化算法。
A new algorithm based on neural network models is also presented, in which the neural networks are employed to express the membership function of fuzzy sets and solve the optimization problems.
该算法分别采用神经网络模型进行模糊集隶属函数的表达及优化问题的求解，从而将模糊优化同神经网络有机地结合起来。
In addition, the paper makes use of Genetic Algorithms to optimize learning rates and inertia coefficients of Fuzzy-neural network, which can ensure that the controller achieves optimization control.
此外，通过遗传算法对模糊神经网络的学习速率和惯性系数等进行了优化，为控制系统实现最优控制提供了有力保证。
In addition, the paper makes use of Genetic Algorithms to optimize learning rates and inertia coefficients of Fuzzy-neural network, which can ensure that the controller achieves optimization control.
此外，通过遗传算法对模糊神经网络的学习速率和惯性系数等进行了优化，为控制系统实现最优控制提供了有力保证。
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