Parameters Optimization of the Heating Furnace Control Systems Based on BP Neural Network Improved by Genetic Algorithm

Qiong Wang, Xiaokan Wang
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引用次数: 3

Abstract

The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model, because the heating furnace for heating treatment with the big inertia, the pure time delay and nonlinear time-varying. Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting (Z-N) method. A heating furnace for the object was simulated with MATLAB, simulation results show that the control system has the quicker response characteristic, the better dynamic characteristic and the quite stronger robustness, which has some promotional value for the control of industrial furnace.
基于遗传算法改进BP神经网络的加热炉控制系统参数优化
传统的基于精确数学模型的PID控制难以保证加热工艺的要求,因为加热炉的热处理具有较大的惯性、纯时滞和非线性时变。提出了一种基于改进BP网络的遗传算法的PID控制器优化变量方法,比经典的临界拟合(Z-N)方法更好地实现了整个热过程的全自动智能控制。利用MATLAB对某加热炉对象进行了仿真,仿真结果表明,该控制系统具有较快的响应特性、较好的动态特性和较强的鲁棒性,对工业加热炉的控制具有一定的推广价值。
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