Practical stability criteria for discrete fractional neural networks in product form design analysis.

In this paper, a neural network approach is suggested to the product design analysis. Namely, fractional-order neural network models are proposed as more flexible mechanism to study product form design. Since control and stability methods are fundamental in the construction and practical significance of a neural network model, appropriate controllers are designed and practical stability criteria are proposed for the fractional-order model under consideration. The stability and control analysis are based on the Lyapunov function method. Examples are elaborated to demonstrate the established results. The proposed modeling approach and the stability results are also applicable to numerous industrial design tasks. • Fractional-order neural network modeling approach is introduced to the product form design. • Appropriate controllers are utilized. • The practical stability notion is adopted to the introduced model. • Practical stability criteria are established using the Lyapunov function technique. • Examples and discussion are also offered to verify and justify the proposed results. [ABSTRACT FROM AUTHOR]