J4 ›› 2009, Vol. 27 ›› Issue (01): 73-.

• 论文 • 上一篇    下一篇

权函数神经网络及在选矿厂能耗预测中的应用

张袅娜1,2,陈 芳1,张德江1   

  1. 1.长春工业大学 电气与电子工程学院, 长春 130012;2.吉林大学 汽车工程学院, 长春 130022
  • 出版日期:2009-01-20 发布日期:2009-07-02
  • 通讯作者: 张袅娜(1972— ),女,吉林九台人, 长春工业大学副教授,硕士生导师,吉林大学博士后,主要从事非线性系统控制、智能控制研究 E-mail:zhangniaona@163.com
  • 作者简介:张袅娜(1972— )|女|吉林九台人| 长春工业大学副教授|硕士生导师|吉林大学博士后|主要从事非线性系统控制、智能控制研究|(Tel)86-13894852035(E-mail)zhangniaona@163.com
  • 基金资助:

    国家科技支撑基金资助项目(2007BAE17B04)

Neural Networks with Weight Functions and Application in Energy Consumption Forecasting of Ore Dressing Plant

ZHANG Niao-na1,2, CHEN Fang1, ZHANG De-jiang1   

  1. 1. College of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China;2. College of Automotive Engineering, Jilin University, Changchun 130022, China
  • Online:2009-01-20 Published:2009-07-02

摘要:

  针对大孤山选矿厂磁选工艺过程的多指标强耦合、时变、非线性和大滞后等特点,使基于数学模型的常规预测方法难以应用问题,提出一种新型的权函数神经网络建立能源消耗预测模型。该模型网络拓扑结构只有输入输出两层,网络权值由传统的常数改为权函数。在权函数构造上,结合选矿厂实际生产过程中所提供的生产数据,根据数据样本间隔距离的大小,分别采用不同的函数作为网络的权函数。训练算法仿真实验表明,该算法计算量小,且建模误差为10-2数量级,取得很好的预测效果,从而克服了传统算法局部极小与收敛速度慢的问题。

关键词: 神经网络, 能耗预测, 权函数

Abstract:

The characteristics of multivariate strong coupling, time varying, nonlinear and long time-delay in the magnetic separation process of Dagushan Ore Dressing Plant,make it difficult to use the conventional methodologies of optimal control based on mathematical model. With simple network topology constituted by input layer and output layer only, the new neural network with weight functions is proposed. The weight is function instead of traditional constant. On constructing of weight function, according to the production data in actual production process of Ore Dressing Plant and the gap of these data,different interpolation functions are selected as the weight functions. Simulation examples show the good performance of this method that little calculation work, high calculation speed, with no local minimum and slow convergence problems. Model mentioned above has minor error and the better prediction effect is obtained.

Key words: neural networks, energy consumption forecasting, weight function

中图分类号: 

  • TP183