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A Control System Based on an Improved Model Auto‐Fusion Cerebellar Perceptron and Its Application to the Consensus Problem
Author(s) -
UCHIYAMA SHOGO,
OBAYASHI MASANAO,
KUREMOTO TAKASHI,
KOBAYASHI KUNIKAZU,
MABU SHINGO
Publication year - 2016
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.11808
Subject(s) - computer science , perceptron , focus (optics) , control (management) , artificial intelligence , fusion , multilayer perceptron , control system , machine learning , artificial neural network , engineering , linguistics , philosophy , physics , optics , electrical engineering
SUMMARY In this paper, we propose a control system based on an improved model auto‐fusion cerebellar perceptron using feedback error learning (FEL) which imitates the human cerebellum, and apply it to the consensus problem of a multiagent system (MAS). It is important to control multiple agents because each of them has its own scale and complexity. Therefore, coordinative control of the MAS for each autonomous decision‐making instance has been taken as the focus. To control MAS, we consider use of FEL related to biological movement control. We also propose an auto‐fusion mechanism for mitigation of neuronal fluctuation. We call the proposed system the “Auto‐Fusion Cerebellar Perceptron Improved Model‐Based Control System (AFCPCS)” here. Through a computer simulation of the MAS consensus problem, we demonstrate the effectiveness of the proposed method.