A Neutrosophic Adaptive Recurrent Neural Network for Time-Varying Matrix Inversion

Abstract

Recently, extensive research has been carried out on the convergence and robustness of a unique class of recurrent neural networks known as zeroing neural networks (ZNN). ZNN dynamics has proven to be highly effective at tackling a wide range of time-varying problems in science and engineering. The use of fuzzy logic systems (FLS) in improving ZNN dynamic systems has been shown to be effective in recent research. neutrosophic logic system (NLS) is known to be a more general and efficient approach than FLS. An improvement of the ZNN design for tackling time-varying matrix inversion problems is proposed in this paper by using an appropriately defined NLS. Particularly, a suitable value obtained as the output of an appropriately designed NLS can be used to dynamically adjust the gain parameter contained in ZNN architecture over time to speed up the convergence of the ZNN model. Numerical experiments show that the NLS-based ZNN model is more effective than the equivalent traditional ZNN model in defining the varying-gain parameter.