Neural Networks and Numerical Analysis, Bruno Despres

Автор: Lin Xiao, Lei Jia
Название: Zeroing Neural Networks: Finite-time Convergence Design, Analysis and Applications
ISBN: 1119985994 ISBN-13(EAN): 9781119985990
Издательство: Wiley
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Цена: 111930.00 T
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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
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Цена: 62820.00 T
Наличие на складе: Есть
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.

Автор: Foucart Simon
Название: Mathematical Pictures at a Data Science Exhibition
ISBN: 1316518884 ISBN-13(EAN): 9781316518885
Издательство: Cambridge University Press
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Цена: 138090.00 T
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Описание: This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in general, and machine learning, optimal recovery, compressive sensing, optimization, and neural networks in particular. It will also be valuable to data scientists seeking mathematical sophistication.

Автор: Yagawa Genki, Oishi Atsuya
Название: Computational Mechanics with Neural Networks
ISBN: 303066113X ISBN-13(EAN): 9783030661137
Издательство: Springer
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Цена: 158380.00 T
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Описание: This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics.

Автор: Venkatesan, Ragav,
Название: Convolutional Neural Networks In Vi
ISBN: 1498770398 ISBN-13(EAN): 9781498770392
Издательство: Taylor&Francis
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Цена: 168430.00 T
Наличие на складе: Невозможна поставка.
Описание: This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner`s guide to engineers or students who want to have a quick start on learning and/or building deep learning systems.

Автор: Jin-Liang Wang; Huai-Ning Wu; Tingwen Huang; Shun-
Название: Analysis and Control of Coupled Neural Networks with Reaction-Diffusion Terms
ISBN: 9811049068 ISBN-13(EAN): 9789811049064
Издательство: Springer
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Цена: 130430.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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: Filippo Maria Bianchi; Enrico Maiorino; Michael C.
Название: Recurrent Neural Networks for Short-Term Load Forecasting
ISBN: 3319703374 ISBN-13(EAN): 9783319703374
Издательство: Springer
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Цена: 51230.00 T
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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
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Цена: 121110.00 T
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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
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Цена: 81050.00 T
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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).

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
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Цена: 60220.00 T
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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: Jin-Liang Wang; Huai-Ning Wu; Tingwen Huang; Shun-
Название: Analysis and Control of Coupled Neural Networks with Reaction-Diffusion Terms
ISBN: 9811352623 ISBN-13(EAN): 9789811352621
Издательство: Springer
Рейтинг:
Цена: 130430.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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: Johnson I. Agbinya
Название: Applied Data Analytics - Principles and Applications
ISBN: 8770220964 ISBN-13(EAN): 9788770220965
Издательство: Taylor&Francis
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Цена: 99010.00 T
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.