A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications

Yuhua Li; MJ Pont; NB Jones

A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications

Yuhua Li
, MJ Pont
, NB Jones

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

Abstract

In this paper, we provide a detailed comparison of multi-layer Perceptron (MLP) and radial basis function (RBF) networks in embedded, microcontroller-based condition monitoring and fault diagnosis applications. On the basis of the studies presented here, it is concluded that the MLP provides similar levels of performance to the RBF network while exerting a low computational load on the processor.

Original language	English
Title of host publication	Unknown Host Publication
Pages	577-592
Number of pages	6
Publication status	Published (in print/issue) - 1999
Event	CONDITION MONITORING `99, PROCEEDINGS - SWANSA, WALES Duration: 1 Jan 1999 → …

Conference

Conference	CONDITION MONITORING `99, PROCEEDINGS
Period	1/01/99 → …

Bibliographical note

International Conference on Condition Monitoring, SWANSA, WALES, APR 12-15, 1999

Keywords

engine misfire detection
neural networks
multi-layer perception
radial basis function
condition monitoring
fault classification

Cite this

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title = "A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications",

abstract = "In this paper, we provide a detailed comparison of multi-layer Perceptron (MLP) and radial basis function (RBF) networks in embedded, microcontroller-based condition monitoring and fault diagnosis applications. On the basis of the studies presented here, it is concluded that the MLP provides similar levels of performance to the RBF network while exerting a low computational load on the processor.",

keywords = "engine misfire detection, neural networks, multi-layer perception, radial basis function, condition monitoring, fault classification",

author = "Yuhua Li and MJ Pont and NB Jones",

note = "International Conference on Condition Monitoring, SWANSA, WALES, APR 12-15, 1999; CONDITION MONITORING `99, PROCEEDINGS ; Conference date: 01-01-1999",

year = "1999",

language = "English",

pages = "577--592",

booktitle = "Unknown Host Publication",

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T1 - A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications

AU - Li, Yuhua

AU - Pont, MJ

AU - Jones, NB

N1 - International Conference on Condition Monitoring, SWANSA, WALES, APR 12-15, 1999

PY - 1999

Y1 - 1999

N2 - In this paper, we provide a detailed comparison of multi-layer Perceptron (MLP) and radial basis function (RBF) networks in embedded, microcontroller-based condition monitoring and fault diagnosis applications. On the basis of the studies presented here, it is concluded that the MLP provides similar levels of performance to the RBF network while exerting a low computational load on the processor.

AB - In this paper, we provide a detailed comparison of multi-layer Perceptron (MLP) and radial basis function (RBF) networks in embedded, microcontroller-based condition monitoring and fault diagnosis applications. On the basis of the studies presented here, it is concluded that the MLP provides similar levels of performance to the RBF network while exerting a low computational load on the processor.

KW - engine misfire detection

KW - neural networks

KW - multi-layer perception

KW - radial basis function

KW - condition monitoring

KW - fault classification

M3 - Conference contribution

SP - 577

EP - 592

BT - Unknown Host Publication

T2 - CONDITION MONITORING `99, PROCEEDINGS

Y2 - 1 January 1999

ER -

A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications

Abstract

Conference

Bibliographical note

Keywords

Fingerprint

Cite this