A High Speed Convergent Formula for Time-Variant Generalized Sylvester Equation Solving

The construction of novel and faster categories of recurrent neural networks (RNN) is an important and interesting field of study in domains, which are related with engineering and applied mathematics problems. Different nonlinear odd and increasing functions called activation functions (AFs) have been used for the acceleration of the convergence speed of each RNN formula. In the context of this research a faster model based on a novel AF will be constructed and applied for time-varying generalized Sylvester equation (TVGSE) solving, while theoretical convergence analysis and simulation experiment in MATLAB’s environment, will show the effectiveness of the proposed formula.