Neural Dynamics for Time-varying Problems
Advances and Applications
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Product details:
- Edition number 2025
- Publisher Springer Nature Switzerland
- Date of Publication 2 October 2024
- Number of Volumes 1 pieces, Book
- ISBN 9783031685934
- Binding Hardback
- No. of pages202 pages
- Size 235x155 mm
- Language English
- Illustrations XVII, 202 p. 62 illus., 58 illus. in color. Illustrations, black & white 600
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Long description:
This book mainly presents methods based on neural dynamics for the time-varying problems with applications, together with the corresponding theoretical analysis, simulative examples, and physical experiments. Based on these methods, their applications include 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 are also presented. In this book, we present 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, we integrate computational intelligence methods and control theory to design a series of dynamic and noise-resistant discrete neural dynamic methods. The research work not only owns the theoretical guarantee on its convergence, noise resistance, and accuracy, but demonstrate the effectiveness and robustness in solving various optimization and equation solving problems, particularly in handling time-varying problems and noise perturbations. Moreover, by reducing complexity and avoiding matrix inversion operations, the models’ feasibility and practicality are further enhanced.
MoreTable of Contents:
1. Neural Dynamics Based on Control Theoretical Techniques.- 2. Complex-Valued Discrete-Time Neural Dynamics.- 3. Noise-Tolerant Neural Dynamics.- 4. Computational Neural Dynamics.- 5. Discrete Computational Neural Dynamics.- 6. High-Order Robust Discrete-Time Neural Dynamics.- 7. Collaborative Neural Dynamics.
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