Design of state estimators for neural networks with mixed time-varying delays
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摘要: 研究了混合時變時滯(離散時滯和分布時滯)神經網絡的狀態估計問題.離散時滯在一個區間上變化,區間下界不一定為零.通過構造一個新的Lyapunov泛函,結合Jensen積分不等式,可以得到一個時滯相關狀態估計器設計方法,使得誤差系統是全局漸近穩定的,所得結果由線性矩陣不等式形式給出.數值算例證明了本文方法的有效性和優越性.
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關鍵詞:
- 狀態估計 /
- 時變網絡 /
- 時滯 /
- 神經網絡 /
- 線性矩陣不等式(LMI) /
- LYAPUNOV函數
Abstract: The state estimation problem was studied for neural networks with mixed discrete and distributed time-varying delays as well as general activation functions. The discrete time-varying delay varies in an interval, where the lower bound is not fixed to be zero. Defining a novel Lyapunov functional and using the Jensen integral inequality, a delay-interval-dependent criterion is provided to design a state estimator through available output measurements in terms of a linear matrix inequality (LMI), such that the error-state system is globally asymptotically stable. A numerical example was given to illustrate that this result is more effective and less conservative than some existing ones.-
Key words:
- state estimation /
- time-varying networks /
- delays /
- neural networks /
- linear matrix inequalities /
- Lyapunov functions
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