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。

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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。

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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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