APPLICATION OF THE EVOLUTIONARY PARADIGM TO DESIGNING ARCHITEСTURE OF A NEURAL NETWORK FOR RECOGNIZING THE DISTORTED TEXT

Abstract

The paper presents an attempt to apply of evolutionary methods to the design and training of a system for recognizing distorted text.

Over the past decades, artificial neural networks are widely used in many areas of artificial intelligence, such as forecasting, optimization, data analysis, pattern recognition and decision making. Nevertheless, the traditional heuristic approaches to design of multi-layer neural networks are based on the recombination of already existing neural network architectures.

This approach allows us to solve a wide range of problems, but implies compliance with specific conditions for the quality work of algorithms.

The natural analogues of such intelligent systems in living nature, however, are universal enough to adapt to virtually any habitat.

Despite their extreme complexity and limited ability to study their structures, it is known that these structures were formed as a result of the evolutionary process. And if today it is impossible to determine the exact architecture of the links in biological neural systems, then at least one can try to reproduce the very process of their formation in order to obtain a more universal algorithm than those developed to the present moment.

In opposite to them we form the final structure of the core of the classification system by evolutionary process, taking into account the knowledge about the features of the development and construction of the nervous system of vertebrates.

Applying of the approach makes it possible to abstract from the limitations of existing neural network algorithms, caused by the scope of application of specific types of their structures.