Model Design and Exponential State Estimation for Discrete-Time Delayed Memristive Spiking Neural P Systems

Abstract

This paper investigates the exponential state estimation of discrete-time memristive spiking neural P system (MSNPS). The spiking neural P system (SNPS) offers algorithmic support for neural morphology computation and AI chips, boasting advantages such as high performance and efficiency. As a new type of information device, memristors have efficient computing characteristics that integrate memory and computation, and can serve as synapses in SNPS. Hence, to harness the advantages of SNPS and memristors synergistically, this paper presents a pioneering MSNPS circuit model, in which memristors are used to replace resistors in SNPS. Meanwhile, MSNPS mathematical model is constructed based on circuit model. To be more practical, the time delays are analyzed in the system. Then, because of the discreteness of SNPS, the continuous MSNPS is discretized. Moreover, some sufficient conditions for exponential state estimation are established by utilizing Lyapunov functional to MSNPS. Finally, a numerical simulation example is constructed to validate the main findings.

Keywords: memristive spiking neural P systems, discrete-time system, exponential state estimation