提出的自适应粒子群优化算法，用于优化多层前馈神经网络的拓扑结构，提高了神经网络的学习质量和速度。
The structure of multi-layer feedback forward neural network is optimized by improved PSO. Learning quality and training speed of the neural network are improved.
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 fast stochastic global optimization algorithm, particle group optimization algorithm, was used for training the fuzzy neural network.
实验证明，利用粒子群算法对评估函数进行参数优化是可行的，通过大量的训练后这种算法不但有效地提高了象棋程序的水平而且使象棋具有了自学习的能力。
The result of experience has proved the method using PSO to optimize the evaluation function is effectively to enhance the strength of the Chinese-chess program by self-learning.
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