A Varying-Gain Recurrent Neural Network and Its Application to Solving Online Time-Varying Matrix Equation

作者：Zhang, Zhijun^*; Deng, Xianzhi; Qu, Xilong; Liao, Bolin; Kong, Ling-Dong; Li, Lulan

来源：IEEE Access, 2018, 6: 77940-77952.

DOI：10.1109/ACCESS.2018.2884497

• Neural network models
• matrix equations
• time-varying systems
• convergence analysis

In mathematics and engineering fields, solving online time-varying matrix equation P(t)X(t)Q(t) = W(t) problem is fundamental and vital. A novel varying-gain recurrent neural network (VG-RNN) is proposed to obtain the online solution of such time-varying matrix equation problem. Distinguished from the conventional gradient-based neural network (termed as GNN), zeroing neural network (termed as ZNN), and finite-time zeroing neural network (termed as FTZNN), the design parameter of VG-RNN is changing with time t goes. Theoretical analysis proves that VG-RNN achieves super-exponential convergent performance when solving the online time-varying matrix equation. Simulation comparisons illustrate that VG-RNN possesses the capability of faster convergence rate than that of GNN, ZNN, and FTZNN. Besides, activated by different activation functions, the convergence performance of VG-RNN can be improved.

• 出版日期2018
• 单位吉首大学; 湖南工程学院; 华南理工大学