Recurrent neural networks :, Amit Kumar Tyagi, Ajith Abraham
Автор: Lin Xiao, Lei Jia Название: Zeroing Neural Networks: Finite-time Convergence Design, Analysis and Applications ISBN: 1119985994 ISBN-13(EAN): 9781119985990 Издательство: Wiley Рейтинг: Цена: 111930.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: WILSON Название: Architectureprogress In Neural Networks (Axnn) ISBN: 1567500455 ISBN-13(EAN): 9781567500455 Издательство: Elsevier Science Рейтинг: Цена: 70250.00 T Наличие на складе: Невозможна поставка. Описание: This volume has a special thematic focus on the architecture of neural networks. It is part of a series that reviews research in natural and synthetic neural networks, as well as research in modelling, analysis, design, and development of neural networks in software and hardware areas.
Автор: Ivan Stanimirovic? Название: Applied Neural Networks and Soft Computing ISBN: 1773613863 ISBN-13(EAN): 9781773613864 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 164470.00 T Наличие на складе: Невозможна поставка. Описание: Examines the relation between neural networks and soft computing. A neural network is a system of hardware and software designed after the operations of neurons. Applied neural networks have a plethora of applications and the text tries to touch every aspect to give readers a wider perspective.
Автор: Jovan Pehcevski Название: Soft Computing with NeuroFuzzy Systems ISBN: 1774077795 ISBN-13(EAN): 9781774077795 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 156150.00 T Наличие на складе: Нет в наличии. Описание: Covers different topics from soft computing and neuro-fuzzy systems, including intelligent neuro-fuzzy models, adaptive neuro-fuzzy systems, neuro-fuzzy inference systems, and neuro-fuzzy control.
Автор: OMIDVAR Название: Progress In Neural Networksprogress In Neural Networks (Axnn) ISBN: 0893919659 ISBN-13(EAN): 9780893919658 Издательство: Elsevier Science Рейтинг: Цена: 74180.00 T Наличие на складе: Невозможна поставка. Описание: This series reviews research in natural and synthetic neural networks, as well as reviews research in modelling, analysis, design and development of neural networks in software and hardware areas.
Автор: Norgaard M., Ravn O., Poulsen N.K., Hansen L.K. Название: Neural Networks for Modelling and Control of Dynamic Systems / A Practitioner`s Handbook ISBN: 1852332271 ISBN-13(EAN): 9781852332273 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This book provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank. The book is very application-oriented and gives detailed and pragmatic recommendations that guide the user through the plethora of methods suggested in the literature. Furthermore, it attempts to introduce sound working procedures that can lead to efficient neural network solutions. This will make the book invaluable to the practitioner and as a textbook in courses with a significant hands-on component.
Автор: Filippo Maria Bianchi; Enrico Maiorino; Michael C. Название: Recurrent Neural Networks for Short-Term Load Forecasting ISBN: 3319703374 ISBN-13(EAN): 9783319703374 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series.
Автор: Kostadinov Simeon Название: Recurrent Neural Networks with Python Quick Start Guide ISBN: 1789132339 ISBN-13(EAN): 9781789132335 Издательство: Неизвестно Рейтинг: Цена: 40450.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Developers struggle to find an easy to follow learning resource for implementing Recurrent Neural Network(RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve the results. This book will teach you ...
Автор: Alex Graves Название: Supervised Sequence Labelling with Recurrent Neural Networks ISBN: 3642432182 ISBN-13(EAN): 9783642432187 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book offers a complete framework for classifying and transcribing sequential data with recurrent neural networks. It uses state-of-the-art results in speech and handwriting recognition to show the framework in action.
Автор: Danilo P. Mandic, Jonathon A. Chambers Название: Recurrent Neural Networks for Prediction ISBN: 0471495174 ISBN-13(EAN): 9780471495178 Издательство: Wiley Рейтинг: Цена: 184750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Neural networks consist of interconnected groups of neurons which function as processing units and aim to reconstruct the operation of the human brain.
Автор: Barbara Hammer Название: Learning with Recurrent Neural Networks ISBN: 185233343X ISBN-13(EAN): 9781852333430 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Folding networks, a generalization of recurrent neural networks to tree structured inputs, are investigated as a mechanism to learn regularities on classical symbolic data. Also, the architecture, the training mechanism, and several applications in different areas are explained in this work.
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