Abstract:
A novel temperature control method for rice wine fermentation control system is proposed, based on the system consisting of Labview, SIMATIC PLC300 and sensors. In this algorithm, cross-factor Particle Swarm Optimization(PSO) is applied to the BP neural network(MyPSO-BP) Proportion-Integral-Differential(PID) control. The improved particle swarm algorithm initializes the weights and thresholds of neural network, which can be an effective approach to tuning PID parameters on-line, enhancing system stability and robustness and reducing errors. Simulation experiment is performed by Matlab, and simulation result shows that this method compared with the traditional neural network PID controller has a great advantage in temperature control performance.
Key words:
rice wine fermentation temperature control,
BP neural network,
Particle Swarm Optimization(PSO),
Proportion-Integral-Differential (PID) control,
crossover operator,
inertial weight
摘要: 在 Labview组态软件、西门子PLC300和温度传感器构成的温度控制系统基础上,提出一种新的黄酒发酵温度控制系统,将带交叉因子的粒子群优化(PSO)算法应用到BP神经网络(MyPSO-BP)比例积分微分(PID)控制中。改进的PSO算法初始化神经网络的权重和阈 值,可以更好地在线整定PID参数,增强系统的稳定性和鲁棒性,减小误差。对系统进行Matlab仿真实验,结果表明,该系统相较于传统的神经网络PID控制器具有更好的温度控制性能。
关键词:
黄酒发酵温控,
BP神经网络,
粒子群优化,
比例积分微分控制,
交叉因子,
惯性权重
CLC Number:
DIAO Jing-Jing, LIU Zhu, HU Wen-Jiang, XU Bao-Guo. Rice Wine Fermentation Temperature Control System Based on MyPSO-BP[J]. Computer Engineering, 2012, 38(7): 247-249.
赵兢兢, 刘柱, 许文强, 徐保国. 基于MyPSO-BP的黄酒发酵温控系统[J]. 计算机工程, 2012, 38(7): 247-249.