新算法选择很广一类的隐层神经元函数，可以直接求得全局最小点，不存在BP算法的局部极小、收敛速度慢等问题。
The algorithm can get global minimum easily with a wide variety of functions of hidden neurons, and no problems such as local minima and slow rate of convergence are suffered like BP algorithm.
本文首先对箱子约束全局最优化问题提出了一个新的辅助函数，该函数能跳出当前的局部极小点。
In this paper, a new auxiliary function with one parameter on box constrained for escaping the current local minimizer of global optimization problem is proposed.
将混沌机制引入常规BP算法，利用混沌机制固有的全局游动，逃出权值优化过程中存在的局部极小点，解决了网络训练易陷入局部极小点的问题。
Chaotic mechanism is introduced to normal BP algorithm, and the problem of local limit value for network is solved using global moving characteristic of chaotic mechanism is weight optimization.
通过设定逃逸系数，算法在寻优过程中具有了能够跳出局部极小点到达全局最优点的能力。
In the process of optimization, the method has the ability of escaping from the local minimized point and arriving at the global optimal point by setting an escaping coefficient.
针对前向神经网络BP算法由于初始权值选择不当而陷入局部极小点这一缺陷，提出新的全局优化训练算法。
Then some defects such as slow convergence rate and getting into local minimum in BP algorithm are pointed out, and the root of the defects is presented.
针对前向神经网络BP算法由于初始权值选择不当而陷入局部极小点这一缺陷，提出新的全局优化训练算法。
Then some defects such as slow convergence rate and getting into local minimum in BP algorithm are pointed out, and the root of the defects is presented.
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