基于二叉树型分层BP模型的板形模式识别

针对传统最小二乘多项式板形模式识别方法鲁棒性差、各分项物理意义不明确,以及普通BP(back propagation)识别法精度低等问题,选用勒让德多项式作为板形基本模式,提出一种基于二叉树型分层BP的板形模式识别并行计算模型.该模型通过逐层细化预测范围并选用多个神经网络进行递推.实验结果表明,采用此方法不仅增强了系统的抗干扰能力,而且提高了系统的识别精度.

Flatness pattern recognition based on a binary tree hierarchical BP model

Parallel flatness pattern recognition based on a binary tree hierarchical back propagation(BP)model and Legendre orthodoxy polynomial decomposition was presented aiming at the illegibility in physical meaning and poorness in robust stability of traditional flatness defect pattern recognition by the least squares method(LSM)proximity algorithm and the low accuracy of a common BP neuron network.It reduces the prediction range of each network and uses more networks for degree elevation.Experimental results sho...