Spatio-Temporal RBF Neural Networks

Shujaat Khan; Jawwad Ahmad; Alishba Sadiq; Imran Naseem; Muhammad Moinuddin

doi:10.1109/ICEEST.2018.8643322

Spatio-Temporal RBF Neural Networks

Shujaat Khan
, Jawwad Ahmad
, Alishba Sadiq
, Imran Naseem
, Muhammad Moinuddin

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

5 Scopus citations

Abstract

Herein, we propose a spatio-Temporal extension of RBFNN for nonlinear system identification problem. The proposed algorithm employs the concept of time-space orthogonality and separately models the dynamics and nonlinear complexities of the system. The proposed RBF architecture is explored for the estimation of a highly nonlinear system and results are compared with the standard architecture for both the conventional and fractional gradient decent-based learning rules. The spatio-Temporal RBF is shown to perform better than the standard and fractional RBFNNs by achieving fast convergence and significantly reduced estimation error.

Original language	English
Title of host publication	2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538682494
DOIs	https://doi.org/10.1109/ICEEST.2018.8643322
State	Published - 2 Jul 2018
Externally published	Yes

Publication series

Name	2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Adaptive algorithms
Machine learning
Nonlinear system identification
Radial basis function
Spatio-Temporal modelling

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/ICEEST.2018.8643322

Cite this

Khan, S., Ahmad, J., Sadiq, A., Naseem, I., & Moinuddin, M. (2018). Spatio-Temporal RBF Neural Networks. In 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018 Article 8643322 (2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEEST.2018.8643322

Khan, Shujaat ; Ahmad, Jawwad ; Sadiq, Alishba et al. / Spatio-Temporal RBF Neural Networks. 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018).

@inproceedings{ed36be9e419a45289b4e95c152ffd16e,

title = "Spatio-Temporal RBF Neural Networks",

abstract = "Herein, we propose a spatio-Temporal extension of RBFNN for nonlinear system identification problem. The proposed algorithm employs the concept of time-space orthogonality and separately models the dynamics and nonlinear complexities of the system. The proposed RBF architecture is explored for the estimation of a highly nonlinear system and results are compared with the standard architecture for both the conventional and fractional gradient decent-based learning rules. The spatio-Temporal RBF is shown to perform better than the standard and fractional RBFNNs by achieving fast convergence and significantly reduced estimation error.",

keywords = "Adaptive algorithms, Machine learning, Nonlinear system identification, Radial basis function, Spatio-Temporal modelling",

author = "Shujaat Khan and Jawwad Ahmad and Alishba Sadiq and Imran Naseem and Muhammad Moinuddin",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/ICEEST.2018.8643322",

language = "English",

series = "2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018",

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Khan, S, Ahmad, J, Sadiq, A, Naseem, I & Moinuddin, M 2018, Spatio-Temporal RBF Neural Networks. in 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018., 8643322, 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICEEST.2018.8643322

Spatio-Temporal RBF Neural Networks. / Khan, Shujaat; Ahmad, Jawwad; Sadiq, Alishba et al.
2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8643322 (2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018).

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

TY - GEN

T1 - Spatio-Temporal RBF Neural Networks

AU - Khan, Shujaat

AU - Ahmad, Jawwad

AU - Sadiq, Alishba

AU - Naseem, Imran

AU - Moinuddin, Muhammad

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Herein, we propose a spatio-Temporal extension of RBFNN for nonlinear system identification problem. The proposed algorithm employs the concept of time-space orthogonality and separately models the dynamics and nonlinear complexities of the system. The proposed RBF architecture is explored for the estimation of a highly nonlinear system and results are compared with the standard architecture for both the conventional and fractional gradient decent-based learning rules. The spatio-Temporal RBF is shown to perform better than the standard and fractional RBFNNs by achieving fast convergence and significantly reduced estimation error.

AB - Herein, we propose a spatio-Temporal extension of RBFNN for nonlinear system identification problem. The proposed algorithm employs the concept of time-space orthogonality and separately models the dynamics and nonlinear complexities of the system. The proposed RBF architecture is explored for the estimation of a highly nonlinear system and results are compared with the standard architecture for both the conventional and fractional gradient decent-based learning rules. The spatio-Temporal RBF is shown to perform better than the standard and fractional RBFNNs by achieving fast convergence and significantly reduced estimation error.

KW - Adaptive algorithms

KW - Machine learning

KW - Nonlinear system identification

KW - Radial basis function

KW - Spatio-Temporal modelling

UR - https://www.scopus.com/pages/publications/85063422100

U2 - 10.1109/ICEEST.2018.8643322

DO - 10.1109/ICEEST.2018.8643322

M3 - Conference contribution

AN - SCOPUS:85063422100

T3 - 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018

BT - 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Khan S, Ahmad J, Sadiq A, Naseem I, Moinuddin M. Spatio-Temporal RBF Neural Networks. In 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8643322. (2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2018). doi: 10.1109/ICEEST.2018.8643322

Spatio-Temporal RBF Neural Networks

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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