國立虎尾科技大學 |

Neural dynamics for time-varying problems = advances and applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Neural dynamics for time-varying problems/ by Long Jin, Lin Wei, Xin Lv.
其他題名:	advances and applications /
作者:	Jin, Long.
其他作者:	Wei, Lin.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xvii, 202 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-031-68594-1
ISBN:	9783031685941

Neural dynamics for time-varying problems = advances and applications /
Jin, Long.

Neural dynamics for time-varying problemsadvances and applications /[electronic resource] :by Long Jin, Lin Wei, Xin Lv. - Cham :Springer Nature Switzerland :2025. - xvii, 202 p. :ill., digital ;24 cm.

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.

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. Neural Dynamics for Time-varying Problems presents different kinds of neural dynamics models with variant contributions, and further applies these models to diverse scenarios. This book is written for graduate students as well as academic and industrial researchers studying in the developing fields of neural dynamics, computer mathematics, time-varying computation, simulation and modeling, analog hardware, and robotics. It provides a comprehensive view of the combined research of these fields, in addition to its accomplishments, potentials and perspectives.

ISBN: 9783031685941

Standard No.: 10.1007/978-3-031-68594-1doiSubjects--Topical Terms:

677887
Artificial intelligence.

LC Class. No.: TJ217.7

Dewey Class. No.: 629.89

Neural dynamics for time-varying problems = advances and applications /
LDR:02925nam a22003255a 4500 001 1172098
003 DE-He213
005 20250806170211.0
006 m d
007 cr nn 008maaau
008 260625s2025 sz s 0 eng d
020 $a 9783031685941 $q (electronic bk.)
020 $a 9783031685934 $q (paper)
024 7 $a 10.1007/978-3-031-68594-1 $2 doi
035 $a 978-3-031-68594-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TJ217.7
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 629.89 $2 23
090 $a TJ217.7 $b .J61 2025
100 1 $a Jin, Long. $e author. $3 1279487
245 1 0 $a Neural dynamics for time-varying problems $h [electronic resource] : $b advances and applications / $c by Long Jin, Lin Wei, Xin Lv.
260 $a Cham : $c 2025. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a xvii, 202 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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. Neural Dynamics for Time-varying Problems presents different kinds of neural dynamics models with variant contributions, and further applies these models to diverse scenarios. This book is written for graduate students as well as academic and industrial researchers studying in the developing fields of neural dynamics, computer mathematics, time-varying computation, simulation and modeling, analog hardware, and robotics. It provides a comprehensive view of the combined research of these fields, in addition to its accomplishments, potentials and perspectives.
650 0 $a Artificial intelligence. $3 677887
650 0 $a Computational intelligence. $3 720733
650 0 $a System theory. $3 1053911
650 0 $a Control theory. $3 527674
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Systems Theory, Control. $3 669337
700 1 $a Wei, Lin. $3 1502889
700 1 $a Lv, Xin. $3 1502890
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-68594-1
950 $a Artificial Intelligence (R0) (SpringerNature-85269)