Dynamic prediction model for mixed concentrate grade of mineral processing plant

Jinliang Ding; Tianyou Chai; Hong Wang

doi:10.1109/CDC.2010.5717944

Dynamic prediction model for mixed concentrate grade of mineral processing plant

Jinliang Ding, Tianyou Chai, Hong Wang

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

A non-linear modelling approach of dynamic prediction model for mixed concentrate grade consisting of a linear part and a nonlinear part is developed. The nonlinear part is implemented using the least squares support vector machine (LS-SVM), where the problem of selecting model parameters is transformed into the probability distribution function (PDF) control of the modelling error. Both the PDF control based and minimum entropy based model parameter selection approaches are proposed. The experiment results show the effectiveness of the proposed approaches. ©2010 IEEE.

Original language	English
Title of host publication	Proceedings of the IEEE Conference on Decision and Control\|Proc IEEE Conf Decis Control
Pages	6773-6778
Number of pages	5
DOIs	https://doi.org/10.1109/CDC.2010.5717944
Publication status	Published - 2010
Event	2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA Duration: 1 Jul 2010 → …

Conference

Conference	2010 49th IEEE Conference on Decision and Control, CDC 2010
City	Atlanta, GA
Period	1/07/10 → …

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10.1109/CDC.2010.5717944

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title = "Dynamic prediction model for mixed concentrate grade of mineral processing plant",

abstract = "A non-linear modelling approach of dynamic prediction model for mixed concentrate grade consisting of a linear part and a nonlinear part is developed. The nonlinear part is implemented using the least squares support vector machine (LS-SVM), where the problem of selecting model parameters is transformed into the probability distribution function (PDF) control of the modelling error. Both the PDF control based and minimum entropy based model parameter selection approaches are proposed. The experiment results show the effectiveness of the proposed approaches. {\textcopyright}2010 IEEE.",

author = "Jinliang Ding and Tianyou Chai and Hong Wang",

year = "2010",

doi = "10.1109/CDC.2010.5717944",

language = "English",

isbn = "9781424477456",

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note = "2010 49th IEEE Conference on Decision and Control, CDC 2010 ; Conference date: 01-07-2010",

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AU - Chai, Tianyou

AU - Wang, Hong

PY - 2010

Y1 - 2010

N2 - A non-linear modelling approach of dynamic prediction model for mixed concentrate grade consisting of a linear part and a nonlinear part is developed. The nonlinear part is implemented using the least squares support vector machine (LS-SVM), where the problem of selecting model parameters is transformed into the probability distribution function (PDF) control of the modelling error. Both the PDF control based and minimum entropy based model parameter selection approaches are proposed. The experiment results show the effectiveness of the proposed approaches. ©2010 IEEE.

AB - A non-linear modelling approach of dynamic prediction model for mixed concentrate grade consisting of a linear part and a nonlinear part is developed. The nonlinear part is implemented using the least squares support vector machine (LS-SVM), where the problem of selecting model parameters is transformed into the probability distribution function (PDF) control of the modelling error. Both the PDF control based and minimum entropy based model parameter selection approaches are proposed. The experiment results show the effectiveness of the proposed approaches. ©2010 IEEE.

U2 - 10.1109/CDC.2010.5717944

DO - 10.1109/CDC.2010.5717944

M3 - Conference contribution

SN - 9781424477456

SP - 6773

EP - 6778

BT - Proceedings of the IEEE Conference on Decision and Control|Proc IEEE Conf Decis Control

T2 - 2010 49th IEEE Conference on Decision and Control, CDC 2010

Y2 - 1 July 2010

ER -

Dynamic prediction model for mixed concentrate grade of mineral processing plant

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