Zeroing Neural Networks Describes the theoretical and practical
aspects of finite-time ZNN methods for solving an array of
computational problems Zeroing Neural Networks (ZNN) have become
essential tools for solving discretized sensor-driven time-varying
matrix problems in engineering, control theory, and on-chip
applications for robots. Building on the original ZNN model,
finite-time zeroing neural networks (FTZNN) enable efficient,
accurate, and predictive real-time computations. Setting up
discretized FTZNN algorithms for different time-varying matrix
problems requires distinct steps. Zeroing Neural Networks provides
in-depth information on the finite-time 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 time-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: Describes how to
design, analyze, and apply FTZNN models for solving computational
problems Presents multiple FTZNN models for solving time-varying
computational problems Details the noise-tolerance of FTZNN models
to maximize the adaptability of FTZNN models to complex
environments Includes an introduction, problem description, design
scheme, theoretical analysis, illustrative verification,
application, and summary in every chapter Zeroing Neural Networks:
Finite-time Convergence Design, Analysis and Applications is an
essential resource for scientists, researchers, academic lecturers,
and postgraduates in the field, as well as a valuable reference for
engineers and other practitioners working in neurocomputing and
intelligent control.
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