A variance-constrained method to encoding–decoding [formula omitted] state estimation for memristive neural networks with energy harvesting sensor

Abstract

This paper considers the encoding–decoding-based H∞ state estimation issue under variance constraint for delayed memristive neural networks with energy harvesting sensor. In order to improve the transmission security, the encoding–decoding communication mechanism is introduced in the transmission channel, which can encode the transmitted data with limited bits. In addition, the energy level of the energy harvester is described by a random variable that obeys the specific probability distribution, in which the energy harvesting technology is used to provide the required energy and continuously maintain the operation of the sensor. The main objective is to present new encoding–decoding-based H∞ state estimation method such that, in the presence of time-delay and energy harvesting mechanism, the desirable H∞ performance requirement and the estimation error variance constraints are both ensured by providing some sufficient conditions. In the end, the feasibility of new encoding–decoding-based H∞ state estimation algorithm is demonstrated by a simulation experiment.