SHU Han-qi, SONG Qian-kun. Global Stability of Clifford-Valued Recurrent Neural Networks With Mixed Time-Varying Delays[J]. Applied Mathematics and Mechanics, 2017, 38(5): 513-525. doi: 10.21656/1000-0887.370319
Citation: SHU Han-qi, SONG Qian-kun. Global Stability of Clifford-Valued Recurrent Neural Networks With Mixed Time-Varying Delays[J]. Applied Mathematics and Mechanics, 2017, 38(5): 513-525. doi: 10.21656/1000-0887.370319

Global Stability of Clifford-Valued Recurrent Neural Networks With Mixed Time-Varying Delays

doi: 10.21656/1000-0887.370319
Funds:  The National Natural Science Foundation of China(61273021; 61473332)
  • Received Date: 2016-10-17
  • Rev Recd Date: 2017-03-23
  • Publish Date: 2017-05-15
  • The global exponential stability of Clifford-valued recurrent neural networks (RNNs) with both asynchronous time-varying and continuously distributed delays was studied. First, the existence and uniqueness of the equilibrium points of delayed Clifford-valued RNNs were proved with the inequality technique and the M-matrix properties. Then, based on the mathematical analysis method, some determinant conditions ensuring the global exponential stability of such systems were obtained. The simulation results of a numerical example substantiate the effectiveness of the theoretical analysis.
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