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.

翻译：本文针对用于选定动态过程行为（黑箱）建模的人工神经网络结构开发算法问题进行了研究。研究包含四种原创性神经网络架构搜索算法方案。这些算法基于成熟优化技术，例如进化算法和梯度下降法。研究中采用了循环型人工神经网络，其架构通过上述算法以优化方式选定。最优性被理解为在神经网络规模与其对学习所依据的数学模型响应捕捉精度之间达成权衡。在优化过程中，提出了原创性的专门进化算子。研究基于压水式核反应堆内快速过程数学模型生成的数据，开展了扩展验证研究。