LEADER 00820nam 2200253 u 450 001 9910774813103321 005 20250707133443.0 010 $a2-503-60297-5 024 7 $a10.1484/M.IPM-EB.5.131849 035 $a(PPN)281846006 035 $a(CKB)5580000000699837 035 $a(EXLCZ)995580000000699837 100 $a20231107d2023 -u- - 101 0 $aeng 200 04$aThe art of publication from the ninth to the sixteenth century /$fedited by Samu Niskanen, with the assistance of Valentina Rovere 210 1$aTurnhout, Belgium :$cBrepols,$d2023. 311 08$a2-503-60296-7 702 $aNiskanen$b Samu 702 $aRovere$b Valentina 801 2$bUkOxU 906 $aBOOK 912 $a9910774813103321 996 $aThe art of publication from the ninth to the sixteenth century$94130771 997 $aUNINA LEADER 03771nam 22006255 450 001 9910983318003321 005 20250626164427.0 010 $a9783031685941 010 $a3031685946 024 7 $a10.1007/978-3-031-68594-1 035 $a(CKB)36251639200041 035 $a(MiAaPQ)EBC31696295 035 $a(Au-PeEL)EBL31696295 035 $a(OCoLC)1458613496 035 $a(DE-He213)978-3-031-68594-1 035 $a(EXLCZ)9936251639200041 100 $a20241002d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNeural Dynamics for Time-varying Problems $eAdvances and Applications /$fby Long Jin, Lin Wei, Xin Lv 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (213 pages) 311 08$a9783031685934 311 08$a3031685938 327 $a1. 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. 330 $aThis 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. 606 $aArtificial intelligence 606 $aComputational intelligence 606 $aSystem theory 606 $aControl theory 606 $aArtificial Intelligence 606 $aComputational Intelligence 606 $aSystems Theory, Control 615 0$aArtificial intelligence. 615 0$aComputational intelligence. 615 0$aSystem theory. 615 0$aControl theory. 615 14$aArtificial Intelligence. 615 24$aComputational Intelligence. 615 24$aSystems Theory, Control. 676 $a003 700 $aJin$b Long$01784550 701 $aWei$b Lin$01783727 701 $aLv$b Xin$01784551 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983318003321 996 $aNeural Dynamics for Time-Varying Problems$94316185 997 $aUNINA