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.

通过演化算法和梯度下降方法，本研究提出了四种算法来寻找人工神经网络结构，用于行为（黑箱）建模特定的动态过程。在研究中，采用了经过优化选择的循环型人工神经网络，优化的目标是在学习的数学模型响应下，平衡神经网络的规模和准确性。并通过数学模型验证研究中提出的演化算子的有效性。