This paper proposes a design of the self adaptive learning fuzzy controller based on Genetic Algorithms optimization.
本文提出一种基于基因算法优化的自学习模糊控制器的设计。
Considering fuzzy C-means clustering algorithms are sensitive to initialization and easy fall - en to local minimum, a novel optimization method is proposed.
针对模糊C均值聚类算法对初始值敏感、易陷入局部最优的缺陷，提出一种新的优化方法。
Fuzzy logic provides ability for human like conceptualization and reasoning, while evolutionary algorithms are useful for finding optimal solutions of nonlinear and complex optimization problems.
模糊逻辑提供和真人一样的能力概念化和推理，而一种进化算法是用来找最优解非线性和复杂的优化问题。
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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