摘要
真空退火炉中工件温度的精确控制是一个典型的非线性、大时滞、强耦合的复杂控制问题.为实现工件温度的精确控制,通过对退火炉工作机理的分析,以现场实际采集的数据为基础,采用神经网络建立对象模型并利用遗传算法对神经网络的权值、阈值进行优化,提出一种真空退火炉工件温度精确控制的优化数学模型.经仿真研究并将其成果应用于实际控制中,取得了令人满意的效果.
In order to control the work pieces accurately, an optimization model for accurate control temperature of work pieces is proposed. Based on the date gathered from the scene, the mechanism of the vacuum anneal furnace is analyzed. The model based on neural networks is set up and the weights and bias of neural networks are optimized. The model is simulated on an experimental system, and used in practice system. A good result is obtained from the model.
出处
《控制与决策》
EI
CSCD
北大核心
2005年第2期218-221,共4页
Control and Decision
基金
甘肃省科技攻关计划项目(GS015-A52-012)
国家科技攻关计划项目(2002BA901A28).
关键词
真空退火炉
工件
神经网络
遗传算法
建模
优化
Annealing
Computer simulation
Genetic algorithms
Mathematical models
Neural networks
Optimization
Vacuum furnaces
