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Describing the application of artificial neural networks to structural mechanics, this book will be of interest to engineers, computer scientists and mathematicians working on the application of neural computing to structural mechanics and in particular finite element problems. It is accompanied by a voucher for a free software disk.
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). The book is devoted to foundations and applications of NNs in the structural mechanics and design of structures.
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. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.
Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. - Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more - Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing - Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods
Computational Structural Mechanics: Static and Dynamic Behaviors provides a cutting-edge treatment of functionally graded materials and the computational methods and solutions of FG static and vibration problems of plates. Using the Rayleigh-Ritz method, static and dynamic problems related to behavior of FG rectangular, Levy, elliptic, skew and annular plates are discussed in detail. A thorough review of the latest research results, computational methods and applications of FG technology make this an essential resource for researchers in academia and industry. - Explains application-oriented treatments of the functionally graded materials used in industry - Addresses relevant algorithms and key computational techniques - Provides numerical solutions of static and vibration problems associated with functionally graded beams and plates of different geometries
Translation of hugely successful book aimed at advanced undergraduates, graduate students and researchers.
Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
Neurocomputing for Design Automation provides innovative design theories and computational models with two broad objectives: automation and optimization. This singular book: Presents an introduction to the automation and optimization of engineering design of complex engineering systems using neural network computing Outlines new computational models and paradigms for automating the complex process of design for unique engineering systems, such as steel highrise building structures Applies design theories and models to the solution of structural design problems Integrates three computing paradigms: mathematical optimization, neural network computing, and parallel processing The applications described are general enough to be applied directly or by extension to other engineering design problems, such as aerospace or mechanical design. Also, the computational models are shown to be stable and robust - particularly suitable for design automation of large systems, such as a 144-story steel super-highrise building structure with more than 20,000 members. The book provides an exceptional framework for the automation and optimization of engineering design, focusing on a new computing paradigm - neural networks computing. It presents the automation of complex systems at a new and higher level never achieved before.