在网络算法上，提出一种自适应的BP算法，该方法能有效的抑制网络陷于局部极小并缩短了学习时间。
About algorithm, the paper has presented a self-adaptive error BP algorithm which can prevent the networks from getting in the part least and can shorten the studying time.
在个人信用评估部分中，对所有的离散数据进行量化处理，然后使用局部学习率自适应算法，对BP算法加以改进。
In the individual credit evaluation part, it quantifies all kinds of scattered data, and to improve BP algorithm it USES local self-adaptive study rate algorithm.
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)。
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.
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