Abstract
In recent years, artificial intelligence techniques have become fundamental parts of various engineering research activities and practical realizations. The advantages of the neural networks, as one of the main artificial intelligence methods, make them very appropriate for different engineering design problems. However, the qualitative properties of the neural networks’ states are extremely important for their design and practical performance. In addition, the variety of neural network models requires the formulation of appropriate qualitative criteria. This paper studies a class of discrete Bidirectional Associative Memory (BAM) neural networks of the Cohen–Grossberg type that can be applied in engineering design. Due to the nature of the proposed models, they are very suitable for symmetry-related problems. The notion of the practical stability of the states with respect to sets is introduced. The practical stability analysis is conducted by the method of the Lyapunov functions. Examples are presented to verify the proposed criteria and demonstrate the efficiency of the results. Since engineering design is a constrained processes, the obtained stability of the sets’ results can be applied to numerous engineering design tasks of diverse interest.
Highlights
Nowadays, artificial intelligence methods are widely used in every area of engineering research, including engineering design problems
One of the artificial intelligence approaches is related to the use of neural networks that are beneficial tools in decision making, pattern recognition, optimization, classification, and other engineering design problems
An overview of the related literature shows that one of the most applied types of neural networks in engineering design research and practice is the type of feed-forward neural networks, where in most of the proposed neural network models, the input variables represent the body shape, bottom shape, top shape, length and width ratio of the body, function buttons style, number buttons arrangement, screen size, screen mask and function buttons, and outline division style, and the output variables are the desirable product design result
Summary
Artificial intelligence methods are widely used in every area of engineering research, including engineering design problems. Deep learning, programming, and computer-aided techniques are applied in every stage of the engineering design process to help engineers and designers to better express ideas and concepts. The study of artificial intelligence approaches is becoming increasingly important. One of the artificial intelligence approaches is related to the use of neural networks that are beneficial tools in decision making, pattern recognition, optimization, classification, and other engineering design problems. That is why the research on neural networks’ applications in engineering has attracted a tremendous amount of attention, and great progress in the development of this research area has been made [1,2,3,4,5,6]. For a comprehensive foundation of neural networks, we refer to [7]
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