Levenberg-Marquardt algorithm for nonlinear principal component analysis neural network through inputs training

S. Zhao, Yongmao Xu
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引用次数: 12

Abstract

Nonlinear principal component analysis (PCA) through inputs training neural networks (IT-nets) based on gradient descent algorithm is effective in coping with the intrinsic nonlinearity in realistic processes. However, the gradient-based method suffers from the slow convergence behavior after the first few iterations and thus greatly affects its practicability in many cases. In this paper, Levenberg-Marquardt algorithm is introduced to accelerate the training of inputs of the IT-nets. Its efficiency is demonstrated through application to the nonlinear dimensionality reduction of data from an industrial fluidized catalytic cracking (FCC) plant.
Levenberg-Marquardt算法用于非线性主成分分析神经网络通过输入训练
基于梯度下降算法的输入训练神经网络(IT-nets)非线性主成分分析(PCA)可以有效地处理现实过程中的固有非线性。然而,基于梯度的方法在前几次迭代后收敛速度较慢,在许多情况下极大地影响了其实用性。本文引入Levenberg-Marquardt算法来加速it网络输入的训练。通过对某工业流化催化裂化装置数据的非线性降维,验证了该方法的有效性。
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