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: Burden Annette, Burden Richard, Faires J. Douglas Название: Numerical analysis ISBN: 1305253663 ISBN-13(EAN): 9781305253667 Издательство: Cengage Learning Рейтинг: Цена: 9423.00 р. 13462.00-30% Наличие на складе: Есть (1 шт.) Описание: Introduces readers to the theory and application of modern numerical approximation techniques. Providing an accessible treatment that only requires a calculus prerequisite, the authors explain how, why, and when approximation techniques can be expected to work - and why, in some situations, they fail.
Описание: This book introduces selected recent findings on the analysis and control of dynamical behaviors for coupled reaction-diffusion neural networks. It presents novel research ideas and essential definitions concerning coupled reaction-diffusion neural networks, such as passivity, adaptive coupling, spatial diffusion coupling, and the relationship between synchronization and output strict passivity. Further, it gathers research results previously published in many flagship journals, presenting them in a unified form. As such, the book will be of interest to all university researchers and graduate students in Engineering and Mathematics who wish to study the dynamical behaviors of coupled reaction-diffusion neural networks.
Автор: Yu, Francis T.s. (pennsylvania State University, University Park, Usa) Yu, Edward H. Yu, Ann G. Название: Art of learning ISBN: 0815361297 ISBN-13(EAN): 9780815361299 Издательство: Taylor&Francis Рейтинг: Цена: 9033.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The art of learning, associative, cognitive, and creative learning are stated and defined. Learning simplicity, information content as related to neural network learning are discussed. Additionally, the author discusses neural plasticity and adaptability in smarter neural networks.
Автор: Johnson I. Agbinya Название: Applied Data Analytics - Principles and Applications ISBN: 8770220964 ISBN-13(EAN): 9788770220965 Издательство: Taylor&Francis Рейтинг: Цена: 14851.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors.
Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications.
The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts.
This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.
Описание: By the end of the decade, approximately 50 billion devices will be connected over the internet using multiple services such as online gaming, ultra-high definition videos, and 5G mobile services. The associated data traffic demand in both fixed and mobile networks is increasing dramatically, causing network operators to have to migrate the existing optical networks towards next-generation solutions. The main challenge within this development stems from network operators having difficulties finding cost-effective next-generation optical network solutions that can match future high capacity demand in terms of data, reach, and the number of subscribers to support multiple network services on a common network infrastructure. Design, Implementation, and Analysis of Next Generation Optical Networks: Emerging Research and Opportunities is an essential reference source that discusses the next generation of high capacity passive optical access networks (PON) in terms of design, implementation, and analysis and offers a complete reference of technology solutions for next-generation optical networks. Featuring research on topics such as artificial intelligence, electromagnetic interface, and wireless communication, this book is ideally designed for researchers, engineers, scientists, and students interested in understanding, designing, and analyzing the next generation of optical networks.
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: 7685.00 р. 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: Mark Girolami Название: Self-Organising Neural Networks ISBN: 185233066X ISBN-13(EAN): 9781852330668 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume presents the theory and applications of self organizing neural network models which perform the independent component analysis (ICA) transformation and blind source separation (BSS).
Автор: Zenon Waszczysznk Название: Neural Networks in the Analysis and Design of Structures ISBN: 3211833226 ISBN-13(EAN): 9783211833223 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Neural Networks are a new, interdisciplinary tool for information processing. Neurocomputing being successfully introduced to structural problems which are difficult or even impossible to be analysed by standard computers (hard computing).
Описание: This book introduces selected recent findings on the analysis and control of dynamical behaviors for coupled reaction-diffusion neural networks. It presents novel research ideas and essential definitions concerning coupled reaction-diffusion neural networks, such as passivity, adaptive coupling, spatial diffusion coupling, and the relationship between synchronization and output strict passivity. Further, it gathers research results previously published in many flagship journals, presenting them in a unified form. As such, the book will be of interest to all university researchers and graduate students in Engineering and Mathematics who wish to study the dynamical behaviors of coupled reaction-diffusion neural networks.
Описание: This Comprehensive Guide Will Help You Take the Headache Out of Python Coding, Programming, and Data Analysis
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The best way to learn Python is to understand the big picture of all that you need to learn before you dive in and start learning.
Learning Python is easy, even if you've never learned about coding before. It offers excellent readability and simple-to-learn syntax, which helps beginners learn this programming language in no time
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Автор: Rajchakit Grienggrai, Agarwal Praveen, Ramalingam Sriraman Название: Stability Analysis of Neural Networks ISBN: 9811665338 ISBN-13(EAN): 9789811665332 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria.
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