Passivity analysis of memristor-based recurrent neural networks with time-varying delays |
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| Authors: | Shiping Wen Zhigang Zeng Tingwen Huang Yiran Chen |
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| Institution: | 1. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China;3. Texas A & M University at Qatar, Doha 5825, Qatar;4. Department of Electronical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA |
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| Abstract: | This paper investigates the delay-dependent exponential passivity problem of the memristor-based recurrent neural networks (RNNs). Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based RNNs is established. Taking into account of the information of the neuron activation functions and the involved time-varying delays, several improved results with less computational burden and conservatism have been obtained in the sense of Filippov solutions. A numerical example is presented to show the effectiveness of the obtained results. |
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