Nonlinear Activation Function and Its Application on Solving Dynamic Problems Based on Zeroing Neural Network
Zeroing neural network(ZNN)has been widely used to solve time-varying problems since it was proposed because of its fast convergence speed and ability to re-sist external noise interference.However,the convergence speed and anti-interference ability of the existing zeroing neural network models are still not satisfactory.There-fore,to further improve the performance of ZNN,a new fixed-time convergent acti-vation function(FTCAF)is designed in this paper.Then,a fixed-time convergent zeroing neural network(FTCZNN)model is established based on the proposed acti-vation function and this model is applied to solve dynamic Sylvester equation(DSE).Theoretical analysis proves that the FTCZNN model has a fixed time convergence up-per limit and strong anti-interference ability.In addition,numerical simulation results also demonstrate the superior performance of the FTCZNN model.Finally,FTCZNN model is used to realize the trajectory tracking experiment of the robot manipulator.The experimental results once again prove that the FTCZNN model has fast conver-gence speed and strong anti-interference ability,and its practical application ability is also verified.
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