The paper discusses the features of solving a class of problems for pattern recognition using the STM-32 microcontroller. The problem of pattern recognition can be solved on neural networks of different architectures, the main attention is paid to the Hamming neural network model. The features of the implementation of the Hamming network based on the STM-32 microcontroller for the recognition of images entered via the touch screen are analyzed. It is experimentally shown that the network cannot always correctly process the input value and compare it with the reference value of the class for digital test images. This is due to the high degree of similarity of some images and the presence of noise. In conclusion, recommendations on the implementation of neural network algorithms for image processing on microcontrollers are given.

The development of software tools for electronic equipment has led to the development and widespread use of neural network technology. They are used for processing and making decisions based on the received information, which is not discrete, but has a polymorphic essence. Processing entity data and computing decisions requires significant amounts of computing power and device operating memory. This problem does not allow the widespread use of neural network technologies in portable devices and devices based on microcontrollers. The aim of the article – adapt neural network technology for use on portable environments and microcontroller-based electronic devices. The chosen method of implementing a neural network based on the resource-saving Hamming algorithm, and the optimized program code in the C language made it possible to significantly reduce the requirements for the hardware of the device on which this technology can be implemented. The analysis of modern microcontrollers allowed us to choose and apply the optimal power-to-energysaving microcontroller-STM32, which allowed us to implement a simplified neural network on its basis. The developed algorithm was implemented on a debug board with an STM32 microcontroller in a device that allows you to recognize handwritten numbers entered from the touch screen. The created portable device for recognizing handwritten numbers is applicable as a module in other electronic equipment products. The prospects of using the latest variants of implementing neurocomputers on microcontrollers are shown.