Abstract
The research focuses on current issues of industrial production intellectualization based on bioinspired artificial intelligence algorithms. The paper examines the development of an endocrine homeostasis regulation subsystem for the control of complex industrial automation systems and equipment diagnostics based on self-assembly technology for a unified artificial immune system with neuroendocrine interaction. The modified endocrine-immune and endocrine-neural algorithms developed are presented, based on a biological prototype of human body function. An ontological model of the endocrine regulatory subsystem is developed. Simulation and experimental results are presented, highlighting the effectiveness of the research; the average accuracy for the two algorithms is 96%. A comparative analysis of the algorithms was conducted using a series of statistical tests and other machine learning methods. The results show that the endocrine-neural algorithm outperforms the endocrine-immune algorithm by an average of 3% on a specific set of engineering data.