This book presents the design, proposal, development, analysis, modeling, and simulation of various neural dynamic models, along with their respective applications including motion planning of redundant manipulators, filter design, winner-take-all operation, multiple-input multiple-output system configuration, multi-linear tensor equation solving, and manipulability optimization. Specifically, starting from the top-level considerations of hardware implementation, computational intelligence methods and control theory are integrated to design a series of dynamic and noise-resistant discrete neural dynamic methods. The research not only provides theoretical guarantees on convergence, noise resistance, and accuracy but also demonstrates effectiveness and robustness in solving various optimization and equation-solving problems, particularly in handling time-varying issues and noise perturbations. Moreover, by reducing complexity and avoiding matrix inversion operations, the models' feasibility and practicality are further enhanced.