title: State estimation for a class of artificial neural networks with stochastically corrupted measurements under Round-Robin protocol.
abstract: This paper is concerned with the state estimation problem for a class of artificial neural networks (ANNs) without the assumptions of monotonicity or differentiability of the activation functions. The measured outputs are corrupted by stochastic noise signal whose intensity is quantified by a nonlinear function. In order to accommodate the bandwidth limit of the communication channel between the ANN and the state estimator, an equal allocation scheme (i.e. Round-Robin protocol) of the communication resource is employed to effectively mitigate data congestions and save energies. A set of zero-order holders (ZOHs) is utilized to store the received measurements, such that the utilization of the received measurements can be maximized. An update matrix approach is developed to handle the time-varying yet periodic time-delays resulting from the adoption of the Round-Robin protocol. The aim of the proposed problem is to design a state estimator such that the error dynamics is exponentially ultimately bounded. A combination of the Lyapunov stability theory and the stochastic analysis technique is used to derive some easy-to-test conditions for the existence of the desired state estimator. The estimator gains are characterized by the solution to a convex optimization problem that is solved via the semi-definite programme method. Simulation results are given to demonstrate the effectiveness of the proposed estimation approach.
authors.affiliation: Shanghai Key Lab of Modern Optical System, Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
authors.forename: Yuqiang
authors.initials: Y
authors.lastname: Luo
authors.affiliation: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom; Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: zidong.wang@brunel.ac.uk.
authors.forename: Zidong
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Shanghai Key Lab of Modern Optical System, Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. Electronic address: Guoliang.Wei1973@gmail.com.
authors.forename: Guoliang
authors.initials: G
authors.lastname: Wei
authors.affiliation: Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
authors.forename: Fuad E
authors.initials: FE
authors.lastname: Alsaadi
authors.affiliation: Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Department of Mathematics, Quaid-i-Azam University, Islamabad, Pakistan.
authors.forename: Tasawar
authors.initials: T
authors.lastname: Hayat
mesh_terms: D000465:Algorithms; D003142:Communication; D016571:Neural Networks, Computer; D013269:Stochastic Processes; D013997:Time Factors
keywords: Activation function; Artificial neural network; Noise intensity function; Nonlinearity; Round-Robin protocol; State estimation