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English | 2022 | ISBN: 1119985994 | 435 pages | True PDF EPUB | 46.92 MB
Zeroing Neural Networks Describes the theoretical and practical aspects of finite- ZNN methods for solving an array of computational problems
Zeroing Neural Networks (ZNN) have become essential tools for solving discretized sensor-driven -varying matrix problems in eeering, control theory, and on-chip applications for robots.
Building on the original ZNN model, finite- zeroing neural networks (FTZNN) enable efficient, accurate, and predictive real- computations. Setting up discretized FTZNN algorithms for different -varying matrix problems requires distinct steps.
Zeroing Neural Networks provides in-depth information on the finite- convergence of ZNN models in solving computational problems. Divided into eight parts, this comprehensive resource covers modeling methods, theoretical analysis, computer simulations, nonlinear activation functions, and more. Each part focuses on a specific type of -varying computational problem, such as the application of FTZNN to the Lyapunov equation, linear matrix equation, and matrix inversion. Throughout the book, tables explain the performance of different models, while numerous illustrative examples clarify the advantages of each FTZNN method. In addition, the book:
Zeroing Neural Networks: Finite- Convergence Design, Analysis and Applications is an essential resource for scientists, researchers, acad lecturers, and postgraduates in the field, as well as a valuable reference for eeers and other practitioners working in neurocomputing and intelligent control.
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