Static and dynamic approaches to learning in neural networks
Author: Bernardo López Alvaredo
Publisher:
Published: 1997
Total Pages: 115
ISBN-13:
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Static and Dynamic Neural Networks
Author: Madan Gupta
Publisher: John Wiley & Sons
Published: 2004-04-05
Total Pages: 752
ISBN-13: 0471460923
DOWNLOAD EBOOKNeuronale 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.
Deep Learning and Dynamic Neural Networks With Matlab
Author: Perez C.
Publisher: Createspace Independent Publishing Platform
Published: 2017-07-31
Total Pages: 166
ISBN-13: 9781974063505
DOWNLOAD EBOOKDeep 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"
Learning Dynamic and Static Sparse Structures for Deep Neural Networks
Author: Zhourong Chen
Publisher:
Published: 2019
Total Pages: 93
ISBN-13:
DOWNLOAD EBOOKArtificial Neural Networks for Modelling and Control of Non-Linear Systems
Author: Johan A.K. Suykens
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 242
ISBN-13: 1475724934
DOWNLOAD EBOOKArtificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.
Artificial Neural Networks and Machine Learning – ICANN 2021
Author: Igor Farkaš
Publisher: Springer Nature
Published: 2021-09-10
Total Pages: 705
ISBN-13: 3030863832
DOWNLOAD EBOOKThe proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as representation learning, reservoir computing, semi- and unsupervised learning, spiking neural networks, text understanding, transfers and meta learning, and video processing. *The conference was held online 2021 due to the COVID-19 pandemic.
Methods, Models, Simulations and Approaches Towards a General Theory of Change - Proceedings of the Fifth National Conference on Systems Science
Author: Gianfranco Minati
Publisher: World Scientific
Published: 2012
Total Pages: 725
ISBN-13: 9814383325
DOWNLOAD EBOOKThe book contains the Proceedings of the 2010 Conference of the Italian Systems Society. Papers deal with the interdisciplinary study of processes of changing related to a wide variety of specific disciplinary aspects. Classical attempts to deal with them, based on generalising approaches used to study the movement of bodies and environmental influence, have included ineffective reductionistic simplifications. Indeed changing also relates, for instance, to processes of acquisition and varying properties such as for software; growing and aging biological systems; learning/cognitive systems; and socio-economic systems growing and developing through innovations. Some approaches to modelling such processes are based on considering changes in structure, e.g., phase-transitions. Other approaches are based on considering (1) periodic changes in structure as for processes of self-organisation; (2) non-periodic but coherent changes in structure, as for processes of emergence; (3) the quantum level of description. Papers in the book study the problem considering its transdisciplinary nature, i.e., systemic properties studied per se and not within specific disciplinary contexts. The aim of these studies is to outline a transdisciplinary theory of change in systemic properties. Such a theory should have simultaneous, corresponding and eventually hierarchical disciplinary aspects as expected for a general theory of emergence. Within this transdisciplinary context, specific disciplinary research activities and results are assumed to be mutually represented as within a philosophical and conceptual framework based on the theoretical centrality of the observer and conceptual non-separability of context and observer, related to logically open systems and Quantum Entanglement. Contributions deal with such issues in interdisciplinary ways considering theoretical aspects and applications from Physics, Cognitive Science, Biology, Artificial Intelligence, Economics, Architecture, Philosophy, Music and Social Systems.
Predictive Modular Neural Networks
Author: Vassilios Petridis
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 311
ISBN-13: 1461555558
DOWNLOAD EBOOKThe subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.
Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities
Author: Nadia Nedjah
Publisher: Springer Nature
Published: 2022-03-28
Total Pages: 267
ISBN-13: 3030967379
DOWNLOAD EBOOKThis book bridges principles and real-world applications, while also providing thorough theory and technology for the development of artificial intelligence and robots. A lack of cross-pollination between AI and robotics research has led to a lack of progress in both fields. Now that both technologies have made significant strides, there is increased interest in combining the two domains in order to create a new integrated AI and robotics trend. In order to achieve wiser urbanization and more sustainable development, AI in smart cities will play a significant part in equipping the cities with advanced features that will allow residents to safely move about, stroll, shop, and enjoy a more comfortable way of life. If you are a student, researcher, engineer, or professional working in this field, or if you are just curious in the newest advancements in robotics and artificial intelligence for cybersecurity, this book is for you!
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Author: Management Association, Information Resources
Publisher: IGI Global
Published: 2019-10-11
Total Pages: 1671
ISBN-13: 1799804151
DOWNLOAD EBOOKDue to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
