As Particle Swarm Optimization (PSO) may easily get trapped in a local optimum, an improved PSO based on adaptive dynamic neighborhood and comprehensive learning named ADPSO was proposed.
为了克服粒子群算法在求解多峰函数时极易陷入局部最优解的缺陷，提出一种基于自适应动态邻居广义学习的改进粒子群算法(ADPSO)。
The method makes the neural network be self adaptive and difficult to become the local minimum, so that the convergence rate can be greatly speeded and the learning time shortened.
该方法使网络具有自适应能力，从而不易陷入局部最小，导致收敛速度大大加快，训练时间大大缩短。
The self-adaptive learning rate and momentum coefficient are used to avoid the local minimum point in the training process of wavelet neural network.
小波神经网络的训练采用自适应调整学习率及动量系数的方法，以避免陷入局部极小值。
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