Modern Traffic Engineering in the System Approach to the Development of Traffic Networks, El?bieta Macioszek; Grzegorz Sierpi?ski
Автор: Saxena Название: Textbook of Highway & Traffic Engineering ISBN: 8123924178 ISBN-13(EAN): 9788123924175 Издательство: CBS India Рейтинг: Цена: 4572.00 р. 6531.00-30% Наличие на складе: Есть (1 шт.)
Описание: This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gr?bner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Автор: Alireza Rezvanian; Behnaz Moradabadi; Mina Ghavipo Название: Learning Automata Approach for Social Networks ISBN: 3030107663 ISBN-13(EAN): 9783030107666 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis.As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.
Описание: This book is a practical, developer-oriented introduction to deep reinforcement learning (RL). Explore the theoretical concepts of RL, before discovering how deep learning (DL) methods and tools are making it possible to solve more complex and challenging problems than ever before. Apply deep RL methods to training your agent to beat arcade ...
Introduction. The Reason for Paradigm Shift in Transportation Science.- Achievements of Empirical Studies of Traffic Breakdown at Highway Bottlenecks.- Nucleation Nature of Traffic Breakdown - Empirical Fundamentalof Transportation Science.- Failure of Generally Accepted Classical Traffic Flow Theories.- Theoretical Fundamental of Transportation Science - The Three-Phase Theory.- Effect of Automatic Driving on Probability of Breakdown in Traffic Networks.- Future Automatic Driving based on Three-Phase Theory.- The Reason for Incommensurability of Three-Phase Theory with Classical Traffic Flow Theories.- Time-Delayed Breakdown at Traffic Signal in City Traffic.- Theoretical Fundamental of Transportation Science - Breakdown Minimization (BM) Principle.- Maximization of Network Throughput Ensuring Free Flow Conditions in Network.- Minimization of Traffic Congestion in Networks.- Deterioration of Traffic System through Standard Dynamic Traffic Assignment in Networks.- Discussion of Future Dynamic Traffic Assignment and Control in Networks.- Conclusions and Outlook.- Kerner-Klenov Stochastic Microscopic Model in Framework of Three-Phase Theory.- Kerner-Klenov-Schreckenberg-Wolf (KKSW) Cellular Automaton (CA) Three-Phase Model.- Dynamic Traffic Assignment based on Wardrop's UE with Step-by-Step Method.- Glossary.- Index.
Автор: Marco Guerrieri; Raffaele Mauro Название: A Concise Introduction to Traffic Engineering ISBN: 3030607224 ISBN-13(EAN): 9783030607227 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The treatment is concise but it does not neglect to examine the most recent and crucial theoretical aspects which are at the root of numerous highway engineering applications, like, for instance, the essential aspects of highways traffic stream reliability calculation and automated highway systems control.
Описание: This book confronts this old issue head on, with a historical look, incorporating recent advances and new perspectives, thus leading to promising new methods and approaches.
Описание: Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.What You’ll Learn Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks Who This Book Is For Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.
Автор: Somani Название: Survivability and Traffic Grooming in WDM Optical Networks ISBN: 0521369967 ISBN-13(EAN): 9780521369961 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides coverage of survivability (dealing with the risk of losing large volumes of traffic data due to failure of a node or single fiber span) and traffic grooming (managing the increased complexity of smaller user requests over high capacity data pipes); two key issues in modern optical networks.
Автор: Heiko Hamann Название: Swarm Robotics: A Formal Approach ISBN: 3319892797 ISBN-13(EAN): 9783319892795 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides an introduction to Swarm Robotics, which is the application of methods from swarm intelligence to robotics. It goes on to present methods that allow readers to understand how to design large-scale robot systems by going through many example scenarios on topics such as aggregation, coordinated motion (flocking), task allocation, self-assembly, collective construction, and environmental monitoring. The author explains the methodology behind building multiple, simple robots and how the complexity emerges from the multiple interactions between these robots such that they are able to solve difficult tasks. The book can be used as a short textbook for specialized courses or as an introduction to Swarm Robotics for graduate students, researchers, and professionals who want a concise introduction to the field.
Описание: This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gr?bner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Автор: Boris S. Kerner Название: Breakdown in Traffic Networks ISBN: 3662544717 ISBN-13(EAN): 9783662544716 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Introduction. The Reason for Paradigm Shift in Transportation Science.- Achievements of Empirical Studies of Traffic Breakdown at Highway Bottlenecks.- Nucleation Nature of Traffic Breakdown - Empirical Fundamentalof Transportation Science.- Failure of Generally Accepted Classical Traffic Flow Theories.- Theoretical Fundamental of Transportation Science - The Three-Phase Theory.- Effect of Automatic Driving on Probability of Breakdown in Traffic Networks.- Future Automatic Driving based on Three-Phase Theory.- The Reason for Incommensurability of Three-Phase Theory with Classical Traffic Flow Theories.- Time-Delayed Breakdown at Traffic Signal in City Traffic.- Theoretical Fundamental of Transportation Science - Breakdown Minimization (BM) Principle.- Maximization of Network Throughput Ensuring Free Flow Conditions in Network.- Minimization of Traffic Congestion in Networks.- Deterioration of Traffic System through Standard Dynamic Traffic Assignment in Networks.- Discussion of Future Dynamic Traffic Assignment and Control in Networks.- Conclusions and Outlook.- Kerner-Klenov Stochastic Microscopic Model in Framework of Three-Phase Theory.- Kerner-Klenov-Schreckenberg-Wolf (KKSW) Cellular Automaton (CA) Three-Phase Model.- Dynamic Traffic Assignment based on Wardrop's UE with Step-by-Step Method.- Glossary.- Index.
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