Neural Modelling of Dynamic Systems with Time Delays Based on an
Adjusted NEAT Algorithm
- URL: http://arxiv.org/abs/2309.12148v2
- Date: Thu, 2 Nov 2023 10:42:56 GMT
- Title: Neural Modelling of Dynamic Systems with Time Delays Based on an
Adjusted NEAT Algorithm
- Authors: Krzysztof Laddach, Rafa{\l} {\L}angowski
- Abstract summary: The proposed algorithm is based on a well-known NeuroEvolution of Augmenting Topologies (NEAT) algorithm.
The research involved an extended validation study based on data generated from a mathematical model of an exemplary system.
The obtaining simulation results demonstrate the high effectiveness of the devised neural (black-box) models of dynamic systems with time delays.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A problem related to the development of an algorithm designed to find an
architecture of artificial neural network used for black-box modelling of
dynamic systems with time delays has been addressed in this paper. The proposed
algorithm is based on a well-known NeuroEvolution of Augmenting Topologies
(NEAT) algorithm. The NEAT algorithm has been adjusted by allowing additional
connections within an artificial neural network and developing original
specialised evolutionary operators. This resulted in a compromise between the
size of neural network and its accuracy in capturing the response of the
mathematical model under which it has been learnt. The research involved an
extended validation study based on data generated from a mathematical model of
an exemplary system as well as the fast processes occurring in a pressurised
water nuclear reactor. The obtaining simulation results demonstrate the high
effectiveness of the devised neural (black-box) models of dynamic systems with
time delays.
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