A NOVEL ALGORITHM FOR THE MODELING OF COMPLEX PROCESSES

被引：8

作者：

Rubio, Jose de Jesus ^{[1
]}

Lughofer, Edwin ^{[2
]}

Angelov, Plamen ^{[3
]}

Novoa, Juan Francisco ^{[4
]}

Meda-Campana, Jesus A. ^{[4
]}

机构：

[1] Inst Politecn Nacl, Secc Estudios Posgrad & Invest, ESIME Azcapotzalco, Av Granjas 682, Mexico City 02250, DF, Mexico

[2] Johannes Kepler Univ Linz, Dept Knowledge Based Math Syst, Linz, Austria

[3] Univ Lancaster, Sch Comp & Commun, Lancaster, England

[4] Inst Politecn Nacl, Lab Vibrac & Rotodinam, ESIME Zacatenco, Mexico City, DF, Mexico

来源：

KYBERNETIKA | 2018年 / 54卷 / 01期

关键词：

recursive least square; Kalman filter; modeling; complex processes; NEURAL-NETWORK; KALMAN; SYSTEMS; FILTER;

D O I：

10.14736/kyb-2018-1-0079

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples.

引用

页码：79 / 95

页数：17

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