Abstract
This paper is concerned with the problem of event-triggered state estimation for a class of fractional-order neural networks. An event-triggering strategy is proposed to reduce the transmission frequency of the output measurement signals with guaranteed state estimation performance requirements. Based on the Lyapunov method and properties of fractional-order calculus, a sufficient criterion is established for deriving the Mittag–Leffler stability of the estimation error system. By making full use of the properties of Caputo operator and Mittag–Leffler function, the evolution dynamics of measured error is analyzed so as to exclude the unexpected Zeno phenomenon in the event-triggering strategy. Finally, two numerical examples and simulations are provided to show the effectiveness of the theoretical results.
Highlights
Last decades have witnessed the rapid development of the theory of the neural network (NN) because of its wide applications in pattern recognition, signal processing, global optimization, associative memory, parallel computation, classification, and optimization.For NNs, each neuron is usually considered a node which can receive inputs from other nodes or from outside sources
Based on the above discussion, this paper aims to investigate the eventtriggered state estimation (ETSE) for fractional-order neural network (FONN)
We considered the problem of event-triggered state estimation for a class of FONNs
Summary
Last decades have witnessed the rapid development of the theory of the neural network (NN) because of its wide applications in pattern recognition, signal processing, global optimization, associative memory, parallel computation, classification, and optimization.For NNs, each neuron is usually considered a node which can receive inputs from other nodes or from outside sources. Different outputs are generated by different activation functions for fitting a certain target [1,2,3,4] In such practical applications, the state information of neurons is necessary for analyzing the dynamical behaviors of networks, including stability, boundedness or synchronization and carrying out the control design with state feedback [5,6,7,8,9,10,11,12]. The state information of neurons is necessary for analyzing the dynamical behaviors of networks, including stability, boundedness or synchronization and carrying out the control design with state feedback [5,6,7,8,9,10,11,12] It is often difficult, even impossible, to fully acquire the information of neuron state due to some constraints from equipment, resources or techniques.
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