Model of Online Grain Moisture Test System Based on Improved BP Neural Network

摘要：

This paper introduces the multiple sensing mechanism of online moisture test for grain and improved BP neural network.Using BP neural network to construct multi input single-output model,applying gradient descent method with forgetting factor for parameters adjustment of BP neural network,utilizing the nonlinear mapping ability and learning generalization ability of the BP neural network,and using high precision samples for the training of BP neural network,the mathematic model of moisture test system for grain based on BP neural network was established finally.This model overcomes single sensor detection and the method of single curve fitting.Using multi-sensor detection and data processing with neural network,and comprehensively considering the effect on testing output with the aim sensor characteristic and non-aim parameter,the measurement accuracy obtains a great enhancement.The sample testing experiment shows that the model established in this paper has high measurement precision and good reproducibility.

关键词： BP neural network grain moisture online test multi-sensor

作者: Jishun Jiang Hua Ji

作者单位: Department of Electrical and Electronic Engineering Shandong University of Technology Zibo City, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 79-82

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）