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MULTI 4 - Birgitte Westfall - Bog - Gyldendal - Plusbog.dk

MULTI 5 - Peter Mogensen - Bog - Gyldendal - Plusbog.dk

Multi-dimensional Control Problems - Preeti - Bog - Springer Verlag, Singapore - Plusbog.dk

Multi 7 - - Bog - Gyldendal - Plusbog.dk

MULTI 6 - Rikke Teglskov - Bog - Gyldendal - Plusbog.dk

MULTI 6 - Peter Mogensen - Bog - Gyldendal - Plusbog.dk

MULTI 5 - Peter Mogensen - Bog - Gyldendal - Plusbog.dk

MULTI 4 - Peter Mogensen - Bog - Gyldendal - Plusbog.dk

MULTI 9 - Rikke Teglskov - Bog - Gyldendal - Plusbog.dk

MULTI 8 - Rikke Teglskov - Bog - Gyldendal - Plusbog.dk

Multi-Sensor and Multi-Temporal Remote Sensing - Uttara (university Of Allahabad Singh - Bog - Taylor & Francis Ltd - Plusbog.dk

Multi-Sensor and Multi-Temporal Remote Sensing - Uttara (university Of Allahabad Singh - Bog - Taylor & Francis Ltd - Plusbog.dk

This book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the ‘individual sample as mean’ training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields. Key features: - Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes - Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise - Describes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI) - Discusses the role of training data to handle the heterogeneity within a class - Supports multi-sensor and multi-temporal data processing through in-house SMIC software - Includes case studies and practical applications for single class mapping This book is intended for graduate/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.

DKK 854.00
1

Evolutionary Multi-Criterion Optimization - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Multi-Agent Coordination - Amit Konar - Bog - John Wiley & Sons Inc - Plusbog.dk

Multi-Agent Coordination - Amit Konar - Bog - John Wiley & Sons Inc - Plusbog.dk

Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibriumImproving convergence speed of multi-agent Q-learning for cooperative task planningConsensus Q-learning for multi-agent cooperative planningThe efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planningA modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.

DKK 996.00
1

Learning Representation for Multi-View Data Analysis - Handong Zhao - Bog - Springer Nature Switzerland AG - Plusbog.dk

Cloud-based Multi-Modal Information Analytics - Srinidhi Hiriyannaiah - Bog - Taylor & Francis Ltd - Plusbog.dk

The Pocket Guide to Medical Retina - Jason Hsu - Bog - SLACK Incorporated - Plusbog.dk

Cooperative Control of Multi-Agent Systems - Zhisheng Duan - Bog - Taylor & Francis Ltd - Plusbog.dk

Cooperative Control of Multi-Agent Systems - Zhisheng Duan - Bog - Taylor & Francis Ltd - Plusbog.dk

Distributed controller design is generally a challenging task, especially for multi-agent systems with complex dynamics, due to the interconnected effect of the agent dynamics, the interaction graph among agents, and the cooperative control laws. Cooperative Control of Multi-Agent Systems: A Consensus Region Approach offers a systematic framework for designing distributed controllers for multi-agent systems with general linear agent dynamics, linear agent dynamics with uncertainties, and Lipschitz nonlinear agent dynamics. Beginning with an introduction to cooperative control and graph theory, this monograph: - Explores the consensus control problem for continuous-time and discrete-time linear multi-agent systems - Studies the H∞ and H2 consensus problems for linear multi-agent systems subject to external disturbances - Designs distributed adaptive consensus protocols for continuous-time linear multi-agent systems - Considers the distributed tracking control problem for linear multi-agent systems with a leader of nonzero control input - Examines the distributed containment control problem for the case with multiple leaders - Covers the robust cooperative control problem for multi-agent systems with linear nominal agent dynamics subject to heterogeneous matching uncertainties - Discusses the global consensus problem for Lipschitz nonlinear multi-agent systems Cooperative Control of Multi-Agent Systems: A Consensus Region Approach provides a novel approach to designing distributed cooperative protocols for multi-agent systems with complex dynamics. The proposed consensus region decouples the design of the feedback gain matrices of the cooperative protocols from the communication graph and serves as a measure for the robustness of the protocols to variations of the communication graph. By exploiting the decoupling feature, adaptive cooperative protocols are presented that can be designed and implemented in a fully distributed fashion.

DKK 837.00
1

Design and Analysis of Multi-Band Filtering Circuits - Xiaojun Bi - Bog - Springer Verlag, Singapore - Plusbog.dk