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.

翻译：本文研究了一种用于构建时滞动态系统黑箱建模的人工神经网络结构搜索算法。所提算法基于经典的增强拓扑神经进化（NEAT）算法，通过允许神经网络内部建立额外连接并开发原创的专用进化算子对NEAT算法进行改进，从而在网络规模与对学习目标数学模型响应捕捉精度之间取得平衡。研究基于示例系统数学模型生成的数据以及压水核反应堆中的快过程数据开展了扩展性验证，仿真结果表明所构建的时滞动态系统神经（黑箱）模型具有高度有效性。