THE OPTIMAL APPROACH TO TRAINING NEURAL NETWORKS FOR INDUSTRIAL PRODUCTION AND AUTOMATION PURPOSES

The development of an optimal approach to training neural networks for the purposes of industrial production and automation is crucial to improve its efficiency. This approach ensures maximization of results with minimal time and money, accuracy and reliability of network predictions, adaptability and scalability, reliability of systems and reduction of the risk of accidents, minimization of unexpected downtime and associated losses, preservation of knowledge and experience of employees for future generations. The optimal approach to training neural networks in industrial enterprises depends on many factors that need to be taken into account. These factors included: task definition, data collection and preprocessing, network architecture selection, network training, validation and testing, integration into the production environment, continuous learning, working with limited resources, cybersecurity. Optimal training of neural networks allows you to obtain the following advantages: automation of processes, improvement of product quality, increased efficiency, improved quality of forecasting and planning, better maintenance of equipment, robot control, product personalization, increased energy efficiency, ensuring production safety. Despite all the advantages, the formation of an optimal approach to training neural networks for industrial production and automation is associated with a number of problems and challenges. Among them, problems were noted in the field of data collection and preprocessing, architecture selection, integration into the production process, scaling, security and reliability, staff training and support, model portability, as well as problems of model complexity.

Keywords:neural network, optimization, automation, industrial automation, neural network training, neural network