说明：  编写了一个pso优化bp神经网络的程序，应用在分类中。第一步：pso优化bp神经网络得到最优的阈值和权值，第二步bp神经网络把该最优的阈值和权值作为初始阈值和权值，采用动量及自适应学习速率算法进行训练。附件中，是数据和编写的部分程序，tiqushuju是用来提取文本中的数据构造样本集的函数。mubiao是用来构造期望输出的函数。bp是已经编写好的，未使用pso优化的bp神经网络函数。pso是本人编写的pso优化bp神经网络的函数，psobp是采用pso优化的阈值和权值作为bp神经网络的初始权值和阈值进行训练和测试的函数。但是本人编写的粒子群优化bp网络的程序训练效果和测试效果远不如只使用bp的效果。
(Prepared a pso bp neural network optimization, application in the classification. First step: PSO optimization bp neural network to get the optimal threshold and weight, the second step bp neural network to the optimal threshold and weight as the initial threshold value and weight, momentum and adaptive learning rate training algorithm . Accessories, data and written part of the program,. Tiqushuju function is used to extract the text data structure of the sample set. mubiao is used to construct the desired output function. BP is already written, unused PSO optimization bp neural network function. pso pso bp neural network optimization function is written in my psobp PSO optimization threshold and the right values ​ ​ as a function of the initial weights and thresholds of BP neural network training and testing. But I am prepared particle swarm optimization BP network training effect and test the effect is far better than using only the effect of the bp.)