Using a nonlinear mechanism to speed up the neural optimization processes
Zeng‐Guang Hou, Fengshui Jing, Min Tan · 2003
Recurrent neural networks based on gradient descent algorithms have been widely used in computation of various optimization and control problems. With the aid of a nonlinear mechanism, an improved method for accelerating the neural computation of optimization processes is proposed. We analyze its convergence property and compare it with other methods. Finally, we give simulation results to show its effectiveness for high-speed computation.