01269nam a2200337 i 4500991001836739707536121008s2010 riua b 001 0 eng d9780821849644b14078569-39ule_instDip.to Matematica e Fisicaeng512.4622AMS 14H51AMS 14H60AMS 14C20LC QA564.A63Aprodu, Marian477511Koszul cohomology and algebraic geometry /Marian Aprodu, Jan NagelProvidence, R. I. :American Mathematical Society,c2010viii, 125 p. :ill. ;26 cmUniversity lecture series,1047-3998 ;52Includes bibliographical references and indexKoszul algebrasGeometry, AlgebraicHomology theoryNagel, Janauthorhttp://id.loc.gov/vocabulary/relators/aut314893.b1407856913-11-1208-10-12991001836739707536LE013 14H APR11 (2010)12013000217871le013pE39.00-l- 00000.i1545703512-11-12Koszul cohomology and algebraic geometry1442487UNISALENTOle01308-10-12ma -engriu0003771nam 22006255 450 991098331800332120250626164427.09783031685941303168594610.1007/978-3-031-68594-1(CKB)36251639200041(MiAaPQ)EBC31696295(Au-PeEL)EBL31696295(OCoLC)1458613496(DE-He213)978-3-031-68594-1(EXLCZ)993625163920004120241002d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierNeural Dynamics for Time-varying Problems Advances and Applications /by Long Jin, Lin Wei, Xin Lv1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (213 pages)9783031685934 3031685938 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.Artificial intelligenceComputational intelligenceSystem theoryControl theoryArtificial IntelligenceComputational IntelligenceSystems Theory, ControlArtificial intelligence.Computational intelligence.System theory.Control theory.Artificial Intelligence.Computational Intelligence.Systems Theory, Control.003Jin Long1784550Wei Lin1783727Lv Xin1784551MiAaPQMiAaPQMiAaPQBOOK9910983318003321Neural Dynamics for Time-Varying Problems4316185UNINA