Product Design Analysis Based on Advanced Neural Network Models: A Review of Recent Findings and Directions for Further Development

Recently, neural network (NN) modeling and analysis approaches have been successfully applied across a wide range of scientific and technological fields, including product design. To build more accurate and advanced NN models, researchers have expanded upon existing findings by incorporating memory effects, reaction–diffusion structures, impulsive perturbations, uncertainties, and fractional-order dynamics. Also, the effective design and real-world application of NN models rely on their qualitative properties. In fact, control and stability are fundamental to the development and practical relevance of an NN model. This paper provides an overview of some advanced NN models and their applications in product design analysis. The author presents his recent findings on innovative NN models and extended stability and control methods, and offers a range of mechanisms that researchers in the field can utilize. The applicability of the proposed modeling approaches and their qualitative analysis to several tasks related to industrial design are also demonstrated. Examples are provided to illustrate the reviewed strategies.