Neural networks and neural dynamics offer robust methods for solving mathematical problems across science, engineering, and business in real-time. Unlike traditional gradient neural networks limited to static problems, zeroing dynamics efficiently handle time-varying challenges.

Zeroing Dynamics, Gradient Dynamics, and Newton Iterations pioneers the exploration of accurate and efficient solutions for time-varying problems using continuous- or discrete-time zeroing dynamics. This book merges research from neural networks, computer mathematics, numerical algorithms, time-varying computation, optimization, simulation, hardware, and fractals.

Discover a thorough examination of both static and dynamic neural network theory, highlighting successful applications of novel theoretical results to practical problems. Explore the development, analysis, modeling, simulation, and comparison of zeroing dynamics models for solving time-varying problems online, including root finding, nonlinear equation solving, matrix inversion, square root finding, quadratic optimization, and inequality resolution.