Abstract
In this study, we propose a modified projection neural network (PNN) with fixed-time convergence to solve the nonlinear projection equations. Under the assumptions of Lipschitz continuity and strict monotonicity, the existence of the solution and the stability in the Lyapunov sense of the proposed modified PNN are proved, which guarantee the convergence in fixed time. The convergence time of the proposed PNN has an upper bound which is independent ofarbitrary initial conditions. Compared with other existing PNN,the presented modified PNN can also solve the non-smooth, nonlinear, and constrained convex optimization problems. In the final step, the numerical simulations demonstrate that the presented modified PNN has a faster convergence rate and gets a more precise solution than the existing methods.
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