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Static And Dynamic Neural Networks
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Book Synopsis Static and Dynamic Neural Networks by : Madan Gupta
Download or read book Static and Dynamic Neural Networks written by Madan Gupta and published by John Wiley & Sons. This book was released on 2004-04-05 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuronale Netze haben sich in vielen Bereichen der Informatik und künstlichen Intelligenz, der Robotik, Prozeßsteuerung und Entscheidungsfindung bewährt. Um solche Netze für immer komplexere Aufgaben entwickeln zu können, benötigen Sie solide Kenntnisse der Theorie statischer und dynamischer neuronaler Netze. Aneignen können Sie sie sich mit diesem Lehrbuch! Alle theoretischen Konzepte sind in anschaulicher Weise mit praktischen Anwendungen verknüpft. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen.
Book Synopsis Static and Dynamic Properties of Neural Networks by : Andrea Crisanti
Download or read book Static and Dynamic Properties of Neural Networks written by Andrea Crisanti and published by . This book was released on 1988 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Perez C. Publisher :Createspace Independent Publishing Platform ISBN 13 :9781974063505 Total Pages :166 pages Book Rating :4.0/5 (635 download)
Book Synopsis Deep Learning and Dynamic Neural Networks With Matlab by : Perez C.
Download or read book Deep Learning and Dynamic Neural Networks With Matlab written by Perez C. and published by Createspace Independent Publishing Platform. This book was released on 2017-07-31 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. Deep learning is especially suited for image recognition, which is important for solving problems such as facial recognition, motion detection, and many advanced driver assistance technologies such as autonomous driving, lane detection, pedestrian detection, and autonomous parking. Neural Network Toolbox provides simple MATLAB commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MATLAB for deep learning, even without knowledge of advanced computer vision algorithms or neural networks. The Neural Network Toolbox software uses the network object to store all of the information that defines a neural network. After a neural network has been created, it needs to be configured and then trained. Configuration involves arranging the network so that it is compatible with the problem you want to solve, as defined by sample data. After the network has been configured, the adjustable network parameters (called weights and biases) need to be tuned, so that the network performance is optimized. This tuning process is referred to as training the network. Configuration and training require that the network be provided with example data. This topic shows how to format the data for presentation to the network. It also explains network configuration and the two forms of network training: incremental training and batch training. Neural networks can be classified into dynamic and static categories. Static (feedforward) networks have no feedback elements and contain no delays; the output is calculated directly from the input through feedforward connections. In dynamic networks, the output depends not only on the current input to the network, but also on the current or previous inputs, outputs, or states of the network. This book develops the following topics: - "Workflow for Neural Network Design" - "Neural Network Architectures" - "Deep Learning in MATLAB" - "Deep Network Using Autoencoders" - "Convolutional Neural Networks" - "Multilayer Neural Networks" - "Dynamic Neural Networks" - "Time Series Neural Networks" - "Multistep Neural Network Prediction"
Book Synopsis Static and dynamic approaches to learning in neural networks by : Bernardo López Alvaredo
Download or read book Static and dynamic approaches to learning in neural networks written by Bernardo López Alvaredo and published by . This book was released on 1997 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deep Learning with MATLAB: Neural Networks Design and Dynamic Neural Networks by : A. Vidales
Download or read book Deep Learning with MATLAB: Neural Networks Design and Dynamic Neural Networks written by A. Vidales and published by Independently Published. This book was released on 2018-12-29 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning Toolbox provides simple MATLAB commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MATLAB for deep learning, even without knowledge of advanced computer vision algorithms or neural networks.Neural networks can be classified into dynamic and static categories. Static (feedforward) networks have no feedback elements and contain no delays; the output is calculated directly from the input through feedforward connections. In dynamic networks, the output depends not only on the current input to the network, but also on the current or previous inputs, outputs, or states of the network.Dynamic networks can be divided into two categories: those that have only feedforward connections, and those that have feedback, or recurrent, connections. To understand the difference between static, feedforward-dynamic, and recurrent-dynamic networks, create some networks and see how they respond to an input sequence.All the specifi dynamic networks discussed so far have either been focused networks,with the dynamics only at the input layer, or feedforward networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network,with feedback connections enclosing several layers of the network. The NARX model isbased on the linear ARX model, which is commonly used in time-series modeling.
Book Synopsis Java for Data Science by : Richard M. Reese
Download or read book Java for Data Science written by Richard M. Reese and published by Packt Publishing Ltd. This book was released on 2017-01-10 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examine the techniques and Java tools supporting the growing field of data science About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples Make your Java applications more capable using machine learning Who This Book Is For This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful. What You Will Learn Understand the nature and key concepts used in the field of data science Grasp how data is collected, cleaned, and processed Become comfortable with key data analysis techniques See specialized analysis techniques centered on machine learning Master the effective visualization of your data Work with the Java APIs and techniques used to perform data analysis In Detail Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application. The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book. Style and approach This book follows a tutorial approach, providing examples of each of the major concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.
Book Synopsis Dynamics of Neural Networks by : Michel J.A.M. van Putten
Download or read book Dynamics of Neural Networks written by Michel J.A.M. van Putten and published by Springer Nature. This book was released on 2020-12-18 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book treats essentials from neurophysiology (Hodgkin–Huxley equations, synaptic transmission, prototype networks of neurons) and related mathematical concepts (dimensionality reductions, equilibria, bifurcations, limit cycles and phase plane analysis). This is subsequently applied in a clinical context, focusing on EEG generation, ischaemia, epilepsy and neurostimulation. The book is based on a graduate course taught by clinicians and mathematicians at the Institute of Technical Medicine at the University of Twente. Throughout the text, the author presents examples of neurological disorders in relation to applied mathematics to assist in disclosing various fundamental properties of the clinical reality at hand. Exercises are provided at the end of each chapter; answers are included. Basic knowledge of calculus, linear algebra, differential equations and familiarity with MATLAB or Python is assumed. Also, students should have some understanding of essentials of (clinical) neurophysiology, although most concepts are summarized in the first chapters. The audience includes advanced undergraduate or graduate students in Biomedical Engineering, Technical Medicine and Biology. Applied mathematicians may find pleasure in learning about the neurophysiology and clinic essentials applications. In addition, clinicians with an interest in dynamics of neural networks may find this book useful, too.
Book Synopsis Robust and Fault-Tolerant Control by : Krzysztof Patan
Download or read book Robust and Fault-Tolerant Control written by Krzysztof Patan and published by Springer. This book was released on 2019-03-16 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include: a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.
Book Synopsis Strategies for Feedback Linearisation by : Freddy Rafael Garces
Download or read book Strategies for Feedback Linearisation written by Freddy Rafael Garces and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using relevant mathematical proofs and case studies illustrating design and application issues, this book demonstrates this powerful technique in the light of research on neural networks, which allow the identification of nonlinear models without the complicated and costly development of models based on physical laws.
Book Synopsis Graph Neural Networks: Foundations, Frontiers, and Applications by : Lingfei Wu
Download or read book Graph Neural Networks: Foundations, Frontiers, and Applications written by Lingfei Wu and published by Springer Nature. This book was released on 2022-01-03 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Book Synopsis Introduction to Neural Networks with Java by : Jeff Heaton
Download or read book Introduction to Neural Networks with Java written by Jeff Heaton and published by Heaton Research Incorporated. This book was released on 2005 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: In addition to showing the programmer how to construct Neural Networks, the book discusses the Java Object Oriented Neural Engine (JOONE), a free open source Java neural engine. (Computers)
Book Synopsis Dynamic and Static Properties of Neural Networks with Feedback by : Avner Priel
Download or read book Dynamic and Static Properties of Neural Networks with Feedback written by Avner Priel and published by . This book was released on 1999 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning Dynamic and Static Sparse Structures for Deep Neural Networks by : Zhourong Chen
Download or read book Learning Dynamic and Static Sparse Structures for Deep Neural Networks written by Zhourong Chen and published by . This book was released on 2019 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Automatic Speech and Speaker Recognition by : Chin-Hui Lee
Download or read book Automatic Speech and Speaker Recognition written by Chin-Hui Lee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization. Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.
Author :Frank C. Hoppensteadt Publisher :Springer Science & Business Media ISBN 13 :1461218284 Total Pages :404 pages Book Rating :4.4/5 (612 download)
Book Synopsis Weakly Connected Neural Networks by : Frank C. Hoppensteadt
Download or read book Weakly Connected Neural Networks written by Frank C. Hoppensteadt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Devoted to local and global analysis of weakly connected systems with applications to neurosciences, this book uses bifurcation theory and canonical models as the major tools of analysis. It presents a systematic and well motivated development of both weakly connected system theory and mathematical neuroscience, addressing bifurcations in neuron and brain dynamics, synaptic organisations of the brain, and the nature of neural codes. The authors present classical results together with the most recent developments in the field, making this a useful reference for researchers and graduate students in various branches of mathematical neuroscience.
Author :Devendra K. Chaturvedi Publisher :Springer Science & Business Media ISBN 13 :3540774807 Total Pages :620 pages Book Rating :4.5/5 (47 download)
Book Synopsis Soft Computing by : Devendra K. Chaturvedi
Download or read book Soft Computing written by Devendra K. Chaturvedi and published by Springer Science & Business Media. This book was released on 2008-08-20 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to some new fields in soft computing with its principal components of fuzzy logic, ANN and EA. The approach in this book is to provide an understanding of the soft computing field and to work through soft computing using examples. It also aims to integrate pseudo-code operational summaries and Matlab codes, to present computer simulation, to include real world applications and to highlight the distinctive work of human consciousness in machine.
Book Synopsis Static and Dynamic Neural Network Modeling for Reinforced Concrete Slab by : Seyed Vahid Razavi Tosee
Download or read book Static and Dynamic Neural Network Modeling for Reinforced Concrete Slab written by Seyed Vahid Razavi Tosee and published by . This book was released on 2012 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt:
