重庆工商大学学报：自然科学版2011，Vol.28Issue(5)：505-508,4.
基于智能蚂蚁算法的脱硫静态模型优化
Static Model Optimization for Desulphurization Based on Intelligent Ant Algorithm
彭燕妮 1王雅娣2
作者信息
- 1. 重庆工商大学计算机科学与信息工程学院,重庆400067
- 2. 宁波波导股份有限公司,宁波315000
- 折叠
摘要
Abstract
In order to search for the law and knowledge of desulphurization process to make decision support for the process,Radial Based Function（RBF） neural network is used as modeling tool.According to the difficulty in determining RBF center and width in the process of modeling,based on the analysis of ant algorithm mechanism,this paper presents that intelligent ant algorithm can be used to make self-adaptation selection for the center and width of RBF neural network model so as to reach optimal balance between the training accuracy and generalization and further to increase predication accuracy of the model.On the basis of analyzing the principle of desulphurization technology and by effective data preprocessing,simulation analysis is conducted,the forecasting accuracy of the model is better than traditional static model for desulphurization,and this model has certain practicability and popularization value.关键词
铁水预脱硫/径向基函数神经网络/信息素/智能蚂蚁算法Key words
predesulphurization of hot metal/RBF neural network/pheromone/intelligent ant algorithm分类
信息技术与安全科学引用本文复制引用
彭燕妮,王雅娣..基于智能蚂蚁算法的脱硫静态模型优化[J].重庆工商大学学报：自然科学版,2011,28(5):505-508,4.
