Neural computation for robust approximate pole assignment

Daniel W.C. Ho; James Lam; Jinhua Xu; Hei Ka Tam

doi:10.1016/S0925-2312(99)00057-0

Neural computation for robust approximate pole assignment

Daniel W.C. Ho, James Lam, Jinhua Xu, Hei Ka Tam

Department of Mathematics

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

33 Citations (Scopus)

Abstract

This paper provides an approach for output feedback robust approximate pole assignment. It is formulated as an unconstrained optimization problem and solved via the gradient flow approach which is ideally suited for neural computing implementation. A schematic circuit architecture of the neural network is suggested. Simulation results are used to demonstrate the effectiveness of the proposed method.

Original language	English
Pages (from-to)	191-211
Journal	Neurocomputing
Volume	25
Issue number	1-3
DOIs	https://doi.org/10.1016/S0925-2312(99)00057-0
Publication status	Published - Apr 1999

Research Keywords

Approximate pole assignment
Gradient flow
Neural networks
Output feedback
Robustness

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title = "Neural computation for robust approximate pole assignment",

abstract = "This paper provides an approach for output feedback robust approximate pole assignment. It is formulated as an unconstrained optimization problem and solved via the gradient flow approach which is ideally suited for neural computing implementation. A schematic circuit architecture of the neural network is suggested. Simulation results are used to demonstrate the effectiveness of the proposed method.",

keywords = "Approximate pole assignment, Gradient flow, Neural networks, Output feedback, Robustness",

author = "Ho, {Daniel W.C.} and James Lam and Jinhua Xu and {Ka Tam}, Hei",

year = "1999",

month = apr,

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language = "English",

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journal = "Neurocomputing",

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TY - JOUR

T1 - Neural computation for robust approximate pole assignment

AU - Ho, Daniel W.C.

AU - Lam, James

AU - Xu, Jinhua

AU - Ka Tam, Hei

PY - 1999/4

Y1 - 1999/4

N2 - This paper provides an approach for output feedback robust approximate pole assignment. It is formulated as an unconstrained optimization problem and solved via the gradient flow approach which is ideally suited for neural computing implementation. A schematic circuit architecture of the neural network is suggested. Simulation results are used to demonstrate the effectiveness of the proposed method.

AB - This paper provides an approach for output feedback robust approximate pole assignment. It is formulated as an unconstrained optimization problem and solved via the gradient flow approach which is ideally suited for neural computing implementation. A schematic circuit architecture of the neural network is suggested. Simulation results are used to demonstrate the effectiveness of the proposed method.

KW - Approximate pole assignment

KW - Gradient flow

KW - Neural networks

KW - Output feedback

KW - Robustness

UR - http://www.scopus.com/inward/record.url?scp=0032962144&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0032962144&origin=recordpage

U2 - 10.1016/S0925-2312(99)00057-0

DO - 10.1016/S0925-2312(99)00057-0

M3 - RGC 21 - Publication in refereed journal

SN - 0925-2312

VL - 25

SP - 191

EP - 211

JO - Neurocomputing

JF - Neurocomputing

IS - 1-3

ER -

Neural computation for robust approximate pole assignment

Abstract

Research Keywords

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