# An automatic selection of optimal recurrent neural network architecture for processes dynamics modelling purposes

arXiv: https://arxiv.org/abs/2309.14037 · HTML: https://mlsanity.com/paper/2309.14037/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 67 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [Guiding Neuroevolution with Structural Objectives](https://mlsanity.com/paper/1902.04346/) (2019)

## Built on by (most cited first, 1 of 1)

- [A new approach for combined model class selection and parameters learning for auto-regressive neural models](https://mlsanity.com/paper/2601.17442/) (2026)

## Abstract (arXiv)

A problem related to the development of algorithms designed to find the structure of artificial neural network used for behavioural (black-box) modelling of selected dynamic processes has been addressed in this paper. The research has included four original proposals of algorithms dedicated to neural network architecture search. Algorithms have been based on well-known optimisation techniques such as evolutionary algorithms and gradient descent methods. In the presented research an artificial neural network of recurrent type has been used, whose architecture has been selected in an optimised way based on the above-mentioned algorithms. The optimality has been understood as achieving a trade-off between the size of the neural network and its accuracy in capturing the response of the mathematical model under which it has been learnt. During the optimisation, original specialised evolutionary operators have been proposed. The research involved an extended validation study based on data generated from a mathematical model of the fast processes occurring in a pressurised water nuclear reactor.