Bacterial foraging algorithm-based optimisation for controlling conditioner temperature of a ring die granulator
by Kun Zhang; Peijian Zhang; Jianguo Wu; Hossein Farid Ghassem Nia; Huiyu Zhou
International Journal of Modelling, Identification and Control (IJMIC), Vol. 22, No. 4, 2014

Abstract: Support vector regression (SVR) is firmly grounded in the framework of statistical learning theory that has been deployed to solve many engineering problems in recent years. To optimise its parameters and achieve optimised results, this paper deploys a novel bacterial foraging algorithm (BFA), combining with support vector machine (SVM) to achieve the good condition temperature prediction of a ring die granulator. With a strong globally searching capability, the proposed method can achieve dynamic optimisation of systematic parameters and overcome the problem of inefficiency in selecting optimal parameters. Simulation results show that the proposed bacterial foraging algorithm is effective in parameter optimisation of the controller for a ring die granulator.

Online publication date: Sat, 27-Dec-2014

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