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Wang Ai-Min
Anyang Normal University

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Application Research based on Artificial Fish-swarm Neural Network in Sintering Process Song Qiang; Wang Ai-Min; Li Hua
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i8.pp6127-6133

Abstract

Sinter tumbler strength is an important parameter in the sintering process, and has an important influence on the performance of finished sinter. Artificial fish swarm algorithm have good ability to acquire the global performance, the neural network has strong nonlinear ability and local optimization performance,; AFSA+BP algorithm combined with artificial fish swarm algorithm and BP algorithm, realizes the complementary artificial fish swarm algorithm global search capability and BP algorithm's local optimization combination of performance, an artificial fish swarm neural results show that the network combination algorithm, it is shown that comparing with the traditional BP neural network forecasting method，the presented forecasting method has better adaptive ability and can give better forecasting results.The artificial fish—swarm algorithm network is trained and checked with the actual production data．this algorithm has strong generalization capability, predictive accuracy improved significantly, and speed up the convergence rate, provides an effective method for strength prediction. Which be used for off-line learning and prediction, a good basis for the online application.
The combination Prediction of BTP in Sintering Process based on Bayesian Framework and LS-SVM SONG Qiang; WANG Ai-min; ZHANG Yun-su
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 8: August 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

The sintering process is a complex process with the characteristics of uncertainty, multivariable coupling, time-varying and time-delay. The Burning-Through-Point (BTP) , which is a import parameter in sintering process , is affected by many reasons and difficult to be controlled to the required precision by conventional control methods. This paper presents a new time-series forecasting methods, which is called Bayesian Least Squares Support Vector Machines（LS-SVM）.  The method applies the Bayesian evidence flame work to infer automatically model parameters of LS-SVM regression.ALS-SVM model is proposed on the basis of the Bayesian LS-SVM models. Several intelligent forecasting key techniques of sintered ore’s chemical components including algorithms of nonlinear SVM in regression approximation, selection of kernel functions and parameters and standardizing of sample data,Bayesian evidence flame-work are studied ; and the control schedules of  BTP based on interval optimization are analyzed. At last, a new intelligent forecasting system of BTP are designed and implemented. Experiment results show that the LS-SVM prediction designed within the Bayesian evidence framework consistently yields good generalization performances, which the method of combining Bayesian theory and LS-SVM is faster and more accurate for the BTP in compare with BP neural network and GM(1,1). DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3087 
Co-Authors Li Hua Song Qiang ZHANG Yun-su