A state estimation method and system for a protocol-based time-delay Markov system

Abstract

The invention discloses a state estimation method for a Markov system with time delay based on a protocol. The state estimation method comprises the steps of building a dynamic model of a neural network system with Markov parameters, sensor nonlinearity, modal-dependent time delay and random interference; building an update matrix according to a sensor selected to transmit data under a given protocol; building a dynamic model of the neural network model under the protocol according to the update matrix and the dynamic model of the neural network system; building an estimator according to the dynamic model of the neural network system under the protocol; calculating a state estimation error according to an estimation state vector of the estimator and a state vector of the neural network system under the protocol; acquiring an estimation augmented system by using the state estimation error; solving a gain matrix of the estimator according to the estimation augmented system by using a system stability judgment theorem; and substituting the gain matrix into the estimator so as to complete estimation for the dynamic model of the neural network system. The invention further discloses a state estimation system for the Markov system with time delay based on the protocol.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a state estimation method and system of a protocol-based time-delay Markov system. Background technique [0002] For decades, due to the extraordinary parallel information processing ability, adaptive ability and self-learning ability of artificial neural network, it has been widely used in the fields of brain science, cognitive science and computer science, so the state estimation problem for artificial neural network has been has received widespread attention. It is worth noting that due to the working environment and other influencing factors, the structure and parameters of the system will undergo uncertain changes, so Markov parameters are a topic that has been favored by everyone in recent years. What is more noteworthy is that in the existing neural network system state estimation research, the problem of communication limitation is rarely considered, so this paper focuse...

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Problems solved by technology

[0004] In view of this, the present invention provides a state estimation method and system based on a protocol-based time-delay Markov system to solve the problem that current state estimation methods cannot simultaneously consider communication limitations, Markov parameters, sensor nonlinearity, Mode-dependent time-delay, random disturbance, and then affect the performance of state estimation

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