A genetic deep learning model for electrophysiological soft robotics
Conference paper
Pandey, H. and Windridge, D. 2021. A genetic deep learning model for electrophysiological soft robotics. Balas, V., Jain, L., Balas, M. and Shahbazova, S. (ed.) 8th International Workshop on Soft Computing Applications. Arad, Romania 13 - 15 Sep 2018 Cham Springer. https://doi.org/10.1007/978-3-030-51992-6_12
Type | Conference paper |
---|---|
Title | A genetic deep learning model for electrophysiological soft robotics |
Authors | Pandey, H. and Windridge, D. |
Abstract | Deep learning methods are modelled by means of multiple layers of predefined set of operations. These days, deep learning techniques utilizing un-supervised learning for training neural networks layers have shown effective results in various fields. Genetic algorithms, by contrast, are search and optimization algorithm that mimic evolutionary process. Previous scientific literatures reveal that genetic algorithms have been successfully implemented for training three-layer neural networks. In this paper, we propose a novel genetic approach to evolving deep learning networks. The performance of the proposed method is evaluated in the context of an electrophysiological soft robot-like system, the results of which demonstrate that our proposed hybrid system is capable of effectively training a deep learning network. |
Keywords | Deep learning; Evolutionary algorithm; Genetic algorithm; Meta-heuristics; Neural networks |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Research Group | Artificial Intelligence group |
Conference | 8th International Workshop on Soft Computing Applications |
Proceedings Title | Soft Computing Applications: Proceedings of the 8th International Workshop Soft Computing Applications (SOFA 2018), Vol. I |
Series | Advances in Intelligent Systems and Computing |
Editors | Balas, V., Jain, L., Balas, M. and Shahbazova, S. |
ISSN | 2194-5357 |
Electronic | 2194-5365 |
ISBN | |
Paperback | 9783030519919 |
Electronic | 9783030519926 |
Publisher | Springer |
Place of publication | Cham |
Publication dates | |
Online | 14 Aug 2020 |
15 Aug 2020 | |
Publication process dates | |
Submitted | 02 May 2018 |
Accepted | 25 Jun 2018 |
Deposited | 29 Jun 2018 |
Output status | Published |
Accepted author manuscript | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-51992-6_12 |
Web address (URL) of conference proceedings | https://doi.org/10.1007/978-3-030-51992-6 |
Language | English |
https://repository.mdx.ac.uk/item/87v25
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