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Evolving Connectionist Systems

Evolving Connectionist Systems PDF Author: Nikola K. Kasabov
Publisher: Springer Science & Business Media
ISBN: 1846283477
Category : Computers
Languages : en
Pages : 465

Book Description
This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.

Evolving Connectionist Systems

Evolving Connectionist Systems PDF Author: Nikola K. Kasabov
Publisher: Springer Science & Business Media
ISBN: 1846283477
Category : Computers
Languages : en
Pages : 465

Book Description
This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.

Evolving Connectionist Systems

Evolving Connectionist Systems PDF Author: Nikola Kasabov
Publisher: Springer
ISBN: 9781848004894
Category : Computers
Languages : en
Pages : 451

Book Description
This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.

Evolving Connectionist Systems

Evolving Connectionist Systems PDF Author: Nikola Kasabov
Publisher: Springer Science & Business Media
ISBN: 144713740X
Category : Computers
Languages : en
Pages : 308

Book Description
Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.

Evolving Connectionist Systems for On-line, Knowledge-based Learning

Evolving Connectionist Systems for On-line, Knowledge-based Learning PDF Author: Nikola K. Kasabov
Publisher:
ISBN:
Category : Automatic speech recognition
Languages : en
Pages : 31

Book Description


Looking for a New AI Paradigm

Looking for a New AI Paradigm PDF Author: Nikola K. Kasabov
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 34

Book Description


Evolving Connectionist Systems and Applications for Dynamic Process Modelling and Control

Evolving Connectionist Systems and Applications for Dynamic Process Modelling and Control PDF Author: Qun Song
Publisher:
ISBN:
Category : Distributed artificial intelligence
Languages : en
Pages : 310

Book Description


Connectionism and the Mind

Connectionism and the Mind PDF Author: William Bechtel
Publisher: Wiley-Blackwell
ISBN: 9780631207139
Category : Philosophy
Languages : en
Pages : 424

Book Description
Connectionism and the Mind provides a clear and balanced introduction to connectionist networks and explores theoretical and philosophical implications. Much of this discussion from the first edition has been updated, and three new chapters have been added on the relation of connectionism to recent work on dynamical systems theory, artificial life, and cognitive neuroscience. Read two of the sample chapters on line: Connectionism and the Dynamical Approach to Cognition: http://www.blackwellpublishing.com/pdf/bechtel.pdf Networks, Robots, and Artificial Life: http://www.blackwellpublishing.com/pdf/bechtel2.pdf

Evolving Connectionist Systems

Evolving Connectionist Systems PDF Author: Michael John Watts
Publisher:
ISBN:
Category : Automatic speech recognition
Languages : en
Pages : 732

Book Description


Artificial Intelligence in the Age of Neural Networks and Brain Computing

Artificial Intelligence in the Age of Neural Networks and Brain Computing PDF Author: Robert Kozma
Publisher: Academic Press
ISBN: 0323958168
Category : Computers
Languages : en
Pages : 398

Book Description
Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence PDF Author: Nikola K. Kasabov
Publisher: Springer
ISBN: 3662577151
Category : Technology & Engineering
Languages : en
Pages : 738

Book Description
Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.