Neural Network-Based Passive Filtering for Delayed Neutral-Type Semi-Markovian Jump Systems

• Published in: IEEE transaction on neural networks and learning systems Vol. 28; no. 9; pp. 2101 - 2114
• Main Authors: Shi, Peng, Li, Fanbiao, Wu, Ligang, Lim, Cheng-Chew
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Attenuation; Complementarity; Computational geometry; Convexity; Decay rate; Delay effects; Delays; Design optimization; Feasibility studies; Filter design (mathematics); Filtering; Markov processes; Neural networks; neural networks (NNs); Neurons; Optimization techniques; semi-Markovian jump systems (S-MJSs); Stability analysis; Stochasticity; Switches; time delay; Timing
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2016.2573853

This paper investigates the problem of exponential passive filtering for a class of stochastic neutral-type neural networks with both semi-Markovian jump parameters and mixed time delays. Our aim is to estimate the states by designing a Luenberger-type observer, such that the filter error dynamics are mean-square exponentially stable with an expected decay rate and an attenuation level. Sufficient conditions for the existence of passive filters are obtained, and a convex optimization algorithm for the filter design is given. In addition, a cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem, which can be readily solved by the existing optimization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed techniques.