ESTIMATION OF ROCK-AGGREGATE VOLUME BASED ON PCA AND LM-OPTIMIZED NEURAL NETWORK  

作　　者：Zhao Pan Chen Ken Wang Yicong Zhang Yun 

机构地区：[1]College of Information Science and Engineering, Ningbo University, Ningbo 315211, China

出　　处：《Journal of Electronics(China)》2009年第6期825-830,共6页电子科学学刊（英文版）

基　　金：Supported by Ningbo Natural Science Foundation (No. 2006A610016);Foundation of Ministry of Education for Returned Overseas Students & Scholars (SRF for ROCS, SEM. No. 2006699)

摘　　要：In granule processing industries, acquisition of particle size and shape parameters is a common procedure, and volumetric measurement is of great importance in dealing with particle sizing and gradation. To eradicate the major drawbacks with manual gauge, this paper proposes an optical approach using Back Propagation （BP） neural network to estimate the particle volume based on the two-Dimensional （2D） image information. To achieve the better network efficiency and structure simplicity, Principal Component Analysis （PCA） is adopted to reduce the dimensions of network inputs To overcome the shortcomings of generic BP network for being slow to converge and vulnerable to being trapped in local minimum, Levenberg-Marquardt （LM） algorithm is applied to achieve a higher speed and a lower error rate. The real particle data is utilized in training and testing the presented network. The experimental result suggests that the proposed neural network is capable of estimating aggregate volume with satisfactory precision and superior to the generic BP network in terms of perforxnance capacity.In granule processing industries,acquisition of particle size and shape parameters is a common procedure,and volumetric measurement is of great importance in dealing with particle sizing and gradation. To eradicate the major drawbacks with manual gauge,this paper proposes an optical approach using Back Propagation (BP) neural network to estimate the particle volume based on the two-Dimensional (2D) image information. To achieve the better network efficiency and structure simplicity,Principal Component Analysis (PCA) is adopted to reduce the dimensions of network inputs. To overcome the shortcomings of generic BP network for being slow to converge and vulnerable to being trapped in local minimum,Levenberg-Marquardt (LM) algorithm is applied to achieve a higher speed and a lower error rate. The real particle data is utilized in training and testing the presented network. The experimental result suggests that the proposed neural network is capable of estimating aggregate volume with satisfactory precision and superior to the generic BP network in terms of performance capacity.

关 键 词：Particle image Particle parameters Principal Component Analysis （PCA） NEURALNETWORK Volume estimation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TQ336.1[自动化与计算机技术—计算机科学与技术]