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Artificial Neural Networks
  • Language: en
  • Pages: 487

Artificial Neural Networks

  • Type: Book
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  • Published: 2014-09-02
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  • Publisher: Springer

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gest...

Operations Research: Closing the Gap Between Research and Practice
  • Language: en
  • Pages: 374

Operations Research: Closing the Gap Between Research and Practice

This book collects selected contributions of the “Optimization and Decision Science - ODS2024 - International conference on the theme of Operations Research: closing the gap between research and practice ODS2024 was held in Badesi (Sardinia, Italy), 8–12 September 2024, and was organized by AIRO, the Italian Operations Research Society. The book offers new and original contributions on operations research, optimization, decision science, and prescriptive analytics from both a methodological and applied perspectives. It provides a state-of-the art on problem models and solving methods to address a widely class of real-world problems, arising in different application areas such as logistic...

Optimization and Decision Science
  • Language: en
  • Pages: 249

Optimization and Decision Science

This book collects selected contributions from the international conference “Optimization and Decision Science” (ODS2020), which was held online on November 19, 2020, and organized by AIRO, the Italian Operations Research Society. The book offers new and original contributions on optimization, decisions science and prescriptive analytics from both a methodological and applied perspective, using models and methods based on continuous and discrete optimization, graph theory and network optimization, analytics, multiple criteria decision making, heuristics, metaheuristics, and exact methods. In addition to more theoretical contributions, the book chapters describe models and methods for add...

Optimization in Green Sustainability and Ecological Transition
  • Language: en
  • Pages: 366

Optimization in Green Sustainability and Ecological Transition

This book collects selected contributions of the “Optimization and Decision Science - ODS2023” international conference on the theme of optimization in green sustainability and ecological transition. ODS2023 was held in Ischia, 4–7 September 2023, and was organized by AIRO, the Italian Operations Research Society. The book offers new and original contributions on operations research, optimization, decision science, and prescriptive analytics from both a methodological and applied perspectives with a special focus on SDG related topics. It provides a state-of-the art on problem models and solving methods to address a widely class of real-world problems, arising in different application ...

Models and Methods in Economics and Management Science
  • Language: en
  • Pages: 254

Models and Methods in Economics and Management Science

With this book, distinguished and notable contributors wish to honor Professor Charles S. Tapiero’s scientific achievements. Although it covers only a few of the directions Professor Tapiero has taken in his work, it presents important modern developments in theory and in diverse applications, as studied by his colleagues and followers, further advancing the topics Tapiero has been investigating. The book is divided into three parts featuring original contributions covering the following areas: general modeling and analysis; applications to marketing, economy and finance; and applications to operations and manufacturing. Professor Tapiero is among the most active researchers in control the...

Artificial Neural Nets and Genetic Algorithms
  • Language: en
  • Pages: 518

Artificial Neural Nets and Genetic Algorithms

The first ICANNGA conference, devoted to biologically inspired computational paradigms, Neural Net works and Genetic Algorithms, was held in Innsbruck, Austria, in 1993. The meeting attracted researchers from all over Europe and further afield, who decided that this particular blend of topics should form a theme for a series of biennial conferences. The second meeting, held in Ales, France, in 1995, carried on the tradition set in Innsbruck of a relaxed and stimulating environment for the. exchange of ideas. The series has continued in Norwich, UK, in 1997, and Portoroz, Slovenia, in 1999. The Institute of Computer Science, Czech Academy of Sciences, is pleased to host the fifth conference in Prague. We have chosen the Liechtenstein palace under the Prague Castle as the conference site to enhance the traditionally good atmosphere of the meeting. There is an inspirational genius loci of the historical center of the city, where four hundred years ago a fruitful combination of theoretical and empirical method, through the collaboration of Johannes Kepler and Tycho de Brahe, led to the discovery of the laws of planetary orbits.

Biologically Inspired Series-Parallel Hybrid Robots
  • Language: en
  • Pages: 514

Biologically Inspired Series-Parallel Hybrid Robots

  • Type: Book
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  • Published: 2024-11-27
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  • Publisher: Elsevier

Biologically Inspired Series-Parallel Hybrid Robots: Design, Analysis and Control provides an extensive review of the state-of-the-art in series-parallel hybrid robots, covering all aspects of their mechatronic system design, modelling, and control. This book highlights the modular and distributed aspects of their mechanical, electronics, and software design, introducing various modern methods for modelling the kinematics and dynamics of complex robots. These methods are also introduced in the form of algorithms or pseudo-code which can be easily programmed with modern programming languages. Presenting case studies on various popular series-parallel hybrid robots which will inspire new robot...

Handbook on Neural Information Processing
  • Language: en
  • Pages: 547

Handbook on Neural Information Processing

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.

Game Theory for Networks
  • Language: en
  • Pages: 202

Game Theory for Networks

T​his book constitutes the refereed proceedings of the 13th EAI International Conference on Game Theory for Networks, GameNets 2025, held in Cambridge, UK, during March 17-18, 2025. The 11 full papers included in this book were carefully reviewed and selected from 28 submissions. They are organized in the following topical sections: Games and Markets; Mechanisms and Games; and Applications of Game Theory.

Machine Learning
  • Language: en
  • Pages: 582

Machine Learning

Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based appr...