Partial-Neurons-Based H∞ State Estimation for Time-Varying Neural Networks Subject to Randomly Occurring Time Delays under Variance Constraint.

This paper discusses the issue of partial-neurons-based H ∞ state estimation for time-varying recurrent neural networks subject to randomly occurring time delays under variance constraint index. The measurement outputs are allowed to be available only at certain neurons. In addition, a random variable is introduced to model the randomly occurring time delays with certain occurrence probability. The aim is to propose the non-augmented partial-neurons-based state estimation strategy. Accordingly, some sufficient conditions are given to ensure two indices including the pre-determined H ∞ performance index and the error variance boundedness via the stochastic analysis approach. Finally, a simulation example is used to demonstrate the feasibility of presented partial-neurons-based H ∞ state estimation algorithm. [ABSTRACT FROM AUTHOR]