A Field Guide To Dynamical Recurrent Networks

A Field Guide to Dynamical Recurrent Networks PDF
Author: John F. Kolen
Publisher: John Wiley & Sons
ISBN: 9780780353695
Size: 61.54 MB
Format: PDF, Kindle
Category : Technology & Engineering
Languages : en
Pages : 464
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Acquire the tools for understanding new architectures and algorithms of dynamical recurrent networks (DRNs) from this valuable field guide, which documents recent forays into artificial intelligence, control theory, and connectionism. This unbiased introduction to DRNs and their application to time-series problems (such as classification and prediction) provides a comprehensive overview of the recent explosion of leading research in this prolific field. A Field Guide to Dynamical Recurrent Networks emphasizes the issues driving the development of this class of network structures. It provides a solid foundation in DRN systems theory and practice using consistent notation and terminology. Theoretical presentations are supplemented with applications ranging from cognitive modeling to financial forecasting. A Field Guide to Dynamical Recurrent Networks will enable engineers, research scientists, academics, and graduate students to apply DRNs to various real-world problems and learn about different areas of active research. It provides both state-of-the-art information and a road map to the future of cutting-edge dynamical recurrent networks.

Handbook Of Dynamic System Modeling

Handbook of Dynamic System Modeling PDF
Author: Paul A. Fishwick
Publisher: CRC Press
ISBN: 9781420010855
Size: 74.82 MB
Format: PDF, ePub
Category : Mathematics
Languages : en
Pages : 760
View: 7648

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The topic of dynamic models tends to be splintered across various disciplines, making it difficult to uniformly study the subject. Moreover, the models have a variety of representations, from traditional mathematical notations to diagrammatic and immersive depictions. Collecting all of these expressions of dynamic models, the Handbook of Dynamic System Modeling explores a panoply of different types of modeling methods available for dynamical systems. Featuring an interdisciplinary, balanced approach, the handbook focuses on both generalized dynamic knowledge and specific models. It first introduces the general concepts, representations, and philosophy of dynamic models, followed by a section on modeling methodologies that explains how to portray designed models on a computer. After addressing scale, heterogeneity, and composition issues, the book covers specific model types that are often characterized by specific visual- or text-based grammars. It concludes with case studies that employ two well-known commercial packages to construct, simulate, and analyze dynamic models. A complete guide to the fundamentals, types, and applications of dynamic models, this handbook shows how systems function and are represented over time and space and illustrates how to select a particular model based on a specific area of interest.

Artificial Neural Networks Icann 2006

Artificial Neural Networks   ICANN 2006 PDF
Author: Stefanos Kollias
Publisher: Springer Science & Business Media
ISBN: 3540386254
Size: 63.42 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 1008
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The two-volume set LNCS 4131 and LNCS 4132 constitutes the refereed proceedings of the 16th International Conference on Artificial Neural Networks, ICANN 2006. The set presents 208 revised full papers, carefully reviewed and selected from 475 submissions. This first volume presents 103 papers, organized in topical sections on feature selection and dimension reduction for regression, learning algorithms, advances in neural network learning methods, ensemble learning, hybrid architectures, and more.

Neural Network Modeling And Identification Of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems PDF
Author: Yury Tiumentsev
Publisher: Academic Press
ISBN: 0128154306
Size: 42.28 MB
Format: PDF, ePub, Docs
Category : Science
Languages : en
Pages : 332
View: 5536

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Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training Offers application examples of dynamic neural network technologies, primarily related to aircraft Provides an overview of recent achievements and future needs in this area

Neural Networks Tricks Of The Trade

Neural Networks  Tricks of the Trade PDF
Author: Grégoire Montavon
Publisher: Springer
ISBN: 3642352898
Size: 39.80 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 769
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The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.

Supervised Sequence Labelling With Recurrent Neural Networks

Supervised Sequence Labelling with Recurrent Neural Networks PDF
Author: Alex Graves
Publisher: Springer Science & Business Media
ISBN: 3642247962
Size: 64.46 MB
Format: PDF
Category : Computers
Languages : en
Pages : 146
View: 6456

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Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.

Innovations In Neural Information Paradigms And Applications

Innovations in Neural Information Paradigms and Applications PDF
Author: Monica Bianchini
Publisher: Springer Science & Business Media
ISBN: 3642040020
Size: 61.11 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 293
View: 7002

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Tremendous advances in all disciplines including engineering, science, health care, business, avionics, management, and so on, can also be attributed to the development of artificial intelligence paradigms. In fact, researchers are always interested in desi- ing machines which can mimic the human behaviour in a limited way. Therefore, the study of neural information processing paradigms have generated great interest among researchers, in that machine learning, borrowing features from human intelligence and applying them as algorithms in a computer friendly way, involves not only Mathem- ics and Computer Science but also Biology, Psychology, Cognition and Philosophy (among many other disciplines). Generally speaking, computers are fundamentally well-suited for performing au- matic computations, based on fixed, programmed rules, i.e. in facing efficiently and reliably monotonous tasks, often extremely time-consuming from a human point of view. Nevertheless, unlike humans, computers have troubles in understanding specific situations, and adapting to new working environments. Artificial intelligence and, in particular, machine learning techniques aim at improving computers behaviour in tackling such complex tasks. On the other hand, humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial intelligence can help us understanding this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities.

Artificial Neural Networks Icann 2009

Artificial Neural Networks     ICANN 2009 PDF
Author: Cesare Alippi
Publisher: Springer
ISBN: 3642042740
Size: 28.98 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 1030
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This volume is part of the two-volume proceedings of the 19th International Conf- ence on Artificial Neural Networks (ICANN 2009), which was held in Cyprus during September 14–17, 2009. The ICANN conference is an annual meeting sp- sored by the European Neural Network Society (ENNS), in cooperation with the - ternational Neural Network Society (INNS) and the Japanese Neural Network Society (JNNS). ICANN 2009 was technically sponsored by the IEEE Computational Intel- gence Society. This series of conferences has been held annually since 1991 in various European countries and covers the field of neurocomputing, learning systems and related areas. Artificial neural networks provide an information-processing structure inspired by biological nervous systems. They consist of a large number of highly interconnected processing elements, with the capability of learning by example. The field of artificial neural networks has evolved significantly in the last two decades, with active partici- tion from diverse fields, such as engineering, computer science, mathematics, artificial intelligence, system theory, biology, operations research, and neuroscience. Artificial neural networks have been widely applied for pattern recognition, control, optimization, image processing, classification, signal processing, etc.

Operations Research Proceedings 2010

Operations Research Proceedings 2010 PDF
Author: Bo Hu
Publisher: Springer Science & Business Media
ISBN: 9783642200090
Size: 39.40 MB
Format: PDF, Docs
Category : Business & Economics
Languages : en
Pages : 667
View: 5953

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This book contains selected papers from the symposium "Operations Research 2010" which was held from September 1-3, 2010 at the "Universität der Bundeswehr München", Germany. The international conference, which also serves as the annual meeting of the German Operations Research Society (GOR), attracted more than 600 participants from more than thirty countries. The general theme "Mastering Complexity" focusses on a natural component of the globalization process. Financial markets, traffic systems, network topologies and, last but not least, energy resource management, all contain complex behaviour and economic interdependencies which necessitate a scientific solution. Operations Research is one of the key instruments to model, simulate and analyze such systems. In the process of developing optimal solutions, suitable heuristics and efficient procedures are some of the challenges which are discussed in this volume.

The Sixth International Symposium On Neural Networks Isnn 2009

The Sixth International Symposium on Neural Networks  ISNN 2009  PDF
Author: Hongwei Wang
Publisher: Springer Science & Business Media
ISBN: 3642012167
Size: 39.65 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 904
View: 112

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This volume of Advances in Soft Computing and Lecture Notes in Computer th Science vols. 5551, 5552 and 5553, constitute the Proceedings of the 6 Inter- tional Symposium of Neural Networks (ISNN 2009) held in Wuhan, China during May 26–29, 2009. ISNN is a prestigious annual symposium on neural networks with past events held in Dalian (2004), Chongqing (2005), Chengdu (2006), N- jing (2007) and Beijing (2008). Over the past few years, ISNN has matured into a well-established series of international conference on neural networks and their applications to other fields. Following this tradition, ISNN 2009 provided an a- demic forum for the participants to disseminate their new research findings and discuss emerging areas of research. Also, it created a stimulating environment for the participants to interact and exchange information on future research challenges and opportunities of neural networks and their applications. ISNN 2009 received 1,235 submissions from about 2,459 authors in 29 co- tries and regions (Australia, Brazil, Canada, China, Democratic People's Republic of Korea, Finland, Germany, Hong Kong, Hungary, India, Islamic Republic of Iran, Japan, Jordan, Macao, Malaysia, Mexico, Norway, Qatar, Republic of Korea, Singapore, Spain, Taiwan, Thailand, Tunisia, United Kingdom, United States, Venezuela, Vietnam, and Yemen) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Based on rigorous reviews by the Program Committee members and reviewers, 95 high-quality papers were selected to be published in this volume.

Artificial Neural Networks Icann 2008

Artificial Neural Networks   ICANN 2008 PDF
Author: Roman Neruda
Publisher: Springer Science & Business Media
ISBN: 3540875352
Size: 63.76 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 1026
View: 5231

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This two volume set LNCS 5163 and LNCS 5164 constitutes the refereed proceedings of the 18th International Conference on Artificial Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The first volume contains papers on mathematical theory of neurocomputing, learning algorithms, kernel methods, statistical learning and ensemble techniques, support vector machines, reinforcement learning, evolutionary computing, hybrid systems, self-organization, control and robotics, signal and time series processing and image processing.

Artificial Neural Networks Icann 2002

Artificial Neural Networks     ICANN 2002 PDF
Author: Jose R. Dorronsoro
Publisher: Springer
ISBN: 3540460845
Size: 48.21 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 1384
View: 1713

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The International Conferences on Arti?cial Neural Networks, ICANN, have been held annually since 1991 and over the years have become the major European meeting in neural networks. This proceedings volume contains all the papers presented at ICANN 2002, the 12th ICANN conference, held in August 28– 30, 2002 at the Escuela T ́ecnica Superior de Inform ́atica of the Universidad Aut ́onoma de Madrid and organized by its Neural Networks group. ICANN 2002 received a very high number of contributions, more than 450. Almost all papers were revised by three independent reviewers, selected among the more than 240 serving at this year’s ICANN, and 221 papers were ?nally selected for publication in these proceedings (due to space considerations, quite a few good contributions had to be left out). I would like to thank the Program Committee and all the reviewers for the great collective e?ort and for helping us to have a high quality conference.

Natural Language Processing Mit Pytorch

Natural Language Processing mit PyTorch PDF
Author: Delip Rao
Publisher: O'Reilly
ISBN: 3960103255
Size: 45.13 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : de
Pages : 250
View: 1050

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Sprachanwendungen wie Amazon Alexa und Google Translate sind heute allgegenwärtig. Grundlage dafür ist das Natural Language Processing (NLP), das zahllose Möglichkeiten für die Entwicklung intelligenter, Deep-Learning-basierter Anwendungen eröffnet. In diesem Buch lernen Sie die neuesten Techniken zur Verarbeitung von Sprache kennen und nutzen dabei das flexible Deep-Learning-Framework PyTorch. Delip Rao und Brian McMahan geben Ihnen einen Überblick über NLP-Methoden und Grundkonzepte neuronaler Netze und demonstrieren Ihnen dann, wie Sie Sprachanwendungen mit PyTorch entwickeln. Der umfangreiche Beispielcode unterstützt Sie dabei, die gezeigten Techniken nachzuvollziehen und auf Ihre konkreten Aufgabenstellungen zu übertragen. Erkunden Sie Berechnungsgraphen und das Paradigma des überwachten Lernens Beherrschen Sie die Grundlagen der PyTorch-Bibliothek, die für Tensor-Manipulationen optimiert wurde Verschaffen Sie sich einen Überblick über traditionelle NLP-Konzepte und -Methoden Machen Sie sich mit den Grundkonzepten von neuronalen Netzen vertraut Untersuchen Sie Feedforward-Netze, wie zum Beispiel das mehrschichtige Perzeptron Verwenden Sie Einbettungen, um Wörter, Sätze, Dokumente und andere Features darzustellen Verstehen Sie, wie sich Sequenzdaten mit rekurrenten neuronalen Netzen modellieren lassen Erkunden Sie Sequenzvoraussagen und generieren Sie Sequenz-zu-Sequenz-Modelle Lernen Sie Entwurfsmuster für den Aufbau von produktionsreifen NLP-Systemen kennen "Ein fantastisches Buch, um in NLP und Deep Learning mit PyTorch einzutauchen. Delip und Brian haben großartige Arbeit geleistet, sie erklären NLP-Konzepte verständlich und demonstrieren sie in jedem Kapitel anhand von Beispielcode, um damit praktische NLPAufgaben zu lösen." — Liling Tan Research Scientist bei Rakuten

Artificial Neural Networks And Machine Learning Icann 2013

Artificial Neural Networks and Machine Learning    ICANN 2013 PDF
Author: Valeri Mladenov
Publisher: Springer
ISBN: 3642407285
Size: 76.93 MB
Format: PDF
Category : Computers
Languages : en
Pages : 643
View: 7548

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The book constitutes the proceedings of the 23rd International Conference on Artificial Neural Networks, ICANN 2013, held in Sofia, Bulgaria, in September 2013. The 78 papers included in the proceedings were carefully reviewed and selected from 128 submissions. The focus of the papers is on following topics: neurofinance graphical network models, brain machine interfaces, evolutionary neural networks, neurodynamics, complex systems, neuroinformatics, neuroengineering, hybrid systems, computational biology, neural hardware, bioinspired embedded systems, and collective intelligence.