FPGA Implementation for Finite-Time and Fixed-Time Neurodynamic Algorithms in Constrained Optimization Problems

Abstract

In this paper, two neurodynamic algorithms and the corresponding Field-Programmable-Gate-Array (FPGA) implementation scheme are presented, respectively. Firstly, based on Lagrange programming neural networks (LPNN) and sliding mode control technique, an algorithm with finite-time convergence and an algorithm with fixed-time convergence is proposed and used to solve constrained optimization problems. Then, the Update module, the Control module, the Gradient module, the finite-time processing (FTP) module, and the fixed-time processing (FxTP) module are designed to form an FPGA hardware implementation of neurodynamic optimization algorithms. The LPNN-based FPGA reconfigurable circuit framework can be structured by invoking the Update module, the Control module, and the Gradient module. The FTP module or the FxTP module can be called into the above framework to form the finite-time or fixed-time stable FPGA reconfiguration framework. Finally, the effectiveness and practicality of the proposed FPGA hardware implementation scheme is verified through example simulations implemented on the Vivado 2019.1 platform.