Delay-partitioning approach design for stochastic stability analysis of uncertain neutral-type neural networks with Markovian jumping parameters

• Novel Lyapunov functions are constructed involving triple and quadruple integrals.
• The delay interval is decomposed into m equivalent subintervals.
• Newton–Leibniz formulas apply in each subinterval and derive weight-free matrices.
• A new inequality is used to reduce conservatism by reciprocally convex inequality.

This paper investigates the problem of stability analysis for uncertain neutral-type neural networks with Markovian jumping parameters and interval time-varying delays. By separating the delay interval into multiple subintervals, a Lyapunov–Krasovskii methodology is established, which contains triple and quadruple integrals. The time-varying delay is considered to locate into any subintervals, which is different from existing delay-partitioning methods. Based on the proposed delay-partitioning approach, a stability criterion is derived to reduce the conservatism. Numerical examples show the effectiveness of the proposed methods.