摘要
建立脱粒装置性能与其影响因素之间非线性模型的目的就是在不确定环境下,对影响脱粒性能的各因素进行优化使脱粒装置的各个性能指标达到最佳要求。它给出了基于GA与BP相结合的具体算法和实现过程。同时针对脱粒装置性能建模这一具体问题,给出了用于对脱粒性能进行建模的基于GA-BP算法神经网络的实现。用DELPHI语言开发的模型仿真软件对脱粒装置进行了性能建模仿真试验,试验结果验证了该模型用于脱粒装置性能建模研究的可行性。
The purpose of setting up nonlinear mathematical model of the threshing performance with its influencing factors is to optimize all parameters of construction and motion so that we can get the optimal threshing performances. We explore how to combine GA algorithm with BP algorithm, give a detailed algorithm and concrete implementation process of GA-BP neural network抯 hybrid studying-training. At the same time, we give a neural network抯 topological structure, which will be used to solve the concrete problem oriented to threshing performance modeling. We have used Delphi programming language to develop a GA-BP neural network抯 simulation software .By using this simulation software, we obtain an experiment on the threshing unit for wheat, the result of test verified the feasibility of threshing performance modeling.
出处
《系统仿真学报》
CAS
CSCD
2003年第9期1294-1296,共3页
Journal of System Simulation
基金
安徽省自然科学基金地面车辆可变形行走机构项目资助(2003kj137)
关键词
脱粒装置
脱粒性能
BP神经网络
遗传算法
仿真
seed-husking plan
threshing performance
BP neural network
genetic algorithm
simulation
