To accelerate the training speed of BP network, a joint-optimized fast BP learning algorithm is proposed.
针对BP网络学习速度的缓慢性，本文提出了一种联合优化后的快速学习算法。
The new algorithm, compared to the BP algorithm, has the fast learning rate and good convergence properties.
该算法有效地改进了神经元网络的学习收敛速度，取得了比常规BP算法更好的收敛性能和学习速度。
The algorithm is applied to XOR problem and nonlinear function approximation. Simulation results show that the chaos-BP algorithm needs shorter learning time than that of the standard BP and fast BP.
采用混合算法对XOR问题和非线性函数进行仿真，结果表明该算法明显优于标准BP算法和快速BP算法。
The algorithm is applied to XOR problem and nonlinear function approximation. Simulation results show that the chaos-BP algorithm needs shorter learning time than that of the standard BP and fast BP.
采用混合算法对XOR问题和非线性函数进行仿真，结果表明该算法明显优于标准BP算法和快速BP算法。
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