Title : 
Observer Design for Switched Recurrent Neural Networks: An Average Dwell Time Approach
         
        
            Author : 
Lian, Jie ; Feng, Zhi ; Shi, Peng
         
        
            Author_Institution : 
Fac. of Electron. Inf. & Electr. Eng., Dalian Univ. of Technol., Dalian, China
         
        
        
        
        
        
        
            Abstract : 
This paper is concerned with the problem of observer design for switched recurrent neural networks with time-varying delay. The attention is focused on designing the full-order observers that guarantee the global exponential stability of the error dynamic system. Based on the average dwell time approach and the free-weighting matrix technique, delay-dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities. The error-state decay estimate is also given. Then, the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results. Finally, four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods.
         
        
            Keywords : 
asymptotic stability; computability; delays; linear matrix inequalities; observers; recurrent neural nets; time-varying systems; average dwell time approach; delay-dependent sufficient condition; error dynamic system; error-state decay estimate; free-weighting matrix technique; global exponential stability; linear matrix inequalities; observer design; solvability; stability analysis; switched recurrent neural network; time-varying delay; Biological neural networks; Delay; Neurons; Observers; Recurrent neural networks; Stability analysis; Switches; Average dwell time method; exponential stability; observer design; switched neural networks; time-varying delay; Algorithms; Artificial Intelligence; Humans; Linear Models; Neural Networks (Computer); Observation; Software Design; Software Validation; Time Factors;
         
        
        
            Journal_Title : 
Neural Networks, IEEE Transactions on
         
        
        
        
        
            DOI : 
10.1109/TNN.2011.2162111