Partial-neurons-based state estimation for artificial neural networks under constrained bit rate: The finite-time case

Joint State and Unknown Input Estimation for a Class of Artificial Neural Networks With Sensor Resolution: An Encoding-Decoding Mechanism

This article is concerned with the joint state and unknown input (SUI) estimation for a class of artificial neural networks (ANNs) with sensor resolution (SR) under the encoding-decoding mechanisms. The consideration of SR, which is an important specification of sensors in the real world, caters to engineering practice. Furthermore, the implementation of the encoding-decoding mechanism in the communication network aims to accommodate the limited bandwidth. The objective of this study is to propose a set-membership estimation algorithm that accurately estimates the state of the ANN without being influenced by the unknown input while accounting for the SR and the encoding-decoding mechanism. First, a sufficient condition is derived to ensure an ellipsoidal constraint on the estimation error. Then, by addressing an optimization problem, the design of the estimator gains is accomplished, and the minimal ellipsoidal constraint on the state estimation error is obtained. Finally, an example is provided to confirm the validity of the proposed joint SUI estimation scheme.

Zonotope-Based Distributed Set-Membership Fusion Estimation for Artificial Neural Networks Under the Dynamic Event-Triggered Mechanism

This article is concerned with the distributed set-membership fusion estimation problem for a class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism (ETM) is utilized to schedule the signal transmission from sensors to local estimators to save resource consumption and avoid data congestion. The main purpose of this article is to design a distributed set-membership fusion estimation algorithm that ensures the global estimation error resides in a zonotope at each time instant and, meanwhile, the radius of the zonotope is ultimately bounded. By means of the zonotope properties and the linear matrix inequality (LMI) technique, the zonotope restraining the prediction error is first calculated to improve the prediction accuracy and subsequently, the zonotope enclosing the local estimation error is derived to enhance the estimation performance. By taking into account the side-effect of the order reduction technique (utilized in designing the local estimation algorithm) of the zonotope, a sufficient condition is derived to guarantee the ultimate boundedness of the radius of the zonotope that encompasses the local estimation error. Furthermore, parameters of the local estimators are obtained via solutions to certain bilinear matrix inequalities. Moreover, the zonotope-based distributed fusion estimator is obtained through minimizing certain upper bound of the radius of the zonotope (that contains the global estimation error) according to the matrix-weighted fusion rule. Finally, the effectiveness of the proposed distributed fusion estimation method is illustrated via a numerical example.