This book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas.
The authors summarize and present the systematic derivations and complete research of ZTD formulas from special 3S-ZTD formulas to general NS-ZTD formulas. These finally lead to their proposed discrete-time Zhang neural network (DTZNN) algorithms, which are more efficient, accurate, and elegant. This book will open the door to scientific and engineering applications of ZTD formulas and neural networks, and will be a major inspiration for studies in neural network modeling, numerical algorithm design, prediction, and robot manipulator control.
The book will benefit engineers, senior undergraduates, graduate students, and researchers in the fields of neural networks, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, robotics, and simulation modeling.
1 Future Matrix Right Pseudoinversion 2 Future Equality-Constrained Quadratic Programming 3 Future Matrix Inversion With Noises 4 Future Matrix Pseudoinversion 5 Future Constrained Nonlinear Optimization With O(g3) 6 Future Unconstrained Nonlinear Optimization With O(g4) 7 Future Different-Layer Inequation-Equation System Solving With O(g5) 8 Future Matrix Square Root Finding With O(g6) 9 Tracking Control of Serial and Parallel Manipulators 10 Future Matrix Inversion with Sometimes-Singular Coefficient Matrix 11 Repetitive Motion Control of Redundant Manipulators 12 Future Different-Layer Equation System Solving 13 Future Matrix Equations Solving 14 Minimum Joint Motion Control of Redundant Manipulators 15 Euler-Precision General Formula of ZTD 16 Lagrange Numerical-Differentiation Formulas
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