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Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
Real-world information is imperfect and is usually described in natural language (NL). Moreover, this information is often partially reliable and a degree of reliability is also expressed in NL. In view of this, the concept of a Z-number is a more adequate concept for the description of real-world information. The main critical problem that naturally arises in processing Z-numbers-based information is the computation with Z-numbers. Nowadays, there is no arithmetic of Z-numbers suggested in existing literature.This book is the first to present a comprehensive and self-contained theory of Z-arithmetic and its applications. Many of the concepts and techniques described in the book, with carefully worked-out examples, are original and appear in the literature for the first time.The book will be helpful for professionals, academics, managers and graduate students in fuzzy logic, decision sciences, artificial intelligence, mathematical economics, and computational economics.
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.
This volume collects together most of the papers presented at the Twelfth Neural Computation and Psychology Workshop (NCPW12) held in 2010 at Birkbeck College (England). The conference invited submissions on neurocomputational models of all cognitive and psychological processes. The special theme of this conference was "From Theory to Applications," which allowed submissions of pure theoretical work and of pure applied work. This topic extended the boundaries of the conference and highlighted the extent to which computational models of cognition and models in general are integrated in the cognitive sciences. The chapters in this book cover a wide range of research topics in neural computation and psychology, including cognitive development, language processing, higher-level cognition, but also ecology-based modeling of cognition, philosophy of science, and real-world applications.
This book covers the issues related to optimization of engineering and management problems using soft computing techniques with an industrial outlook. It covers a broad area related to real life complex decision making problems using a heuristics approach. It also explores a wide perspective and future directions in industrial engineering research on a global platform/scenario. The book highlights the concept of optimization, presents various soft computing techniques, offers sample problems, and discusses related software programs complete with illustrations. Features Explains the concept of optimization and relevance to soft computing techniques towards optimal solution in engineering and management Presents various soft computing techniques Offers problems and their optimization using various soft computing techniques Discusses related software programs, with illustrations Provides a step-by-step tutorial on how to handle relevant software for obtaining the optimal solution to various engineering problems
Although fuzzy systems and neural networks are central to the field of soft computing, most research work has focused on the development of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems, especially their mathematical foundations. Fuzzy Neural Intelligent Systems fills this gap. It develops a mathematical basis for fuzzy neural networks, offers a better way of combining fuzzy logic systems with neural networks, and explores some of their engineering applications. Dividing their focus into three main areas of interest, the authors give a systematic, comprehensive treatment of the relevant concepts and modern practical applications: -Fundamental concepts and theories for fuzzy systems and neural networks. -Foundation for fuzzy neural networks and important related topics -Case examples for neuro-fuzzy systems, fuzzy systems, neural network systems, and fuzzy-neural systems Suitable for self-study, as a reference, and ideal as a textbook, Fuzzy Neural Intelligent Systems is accessible to students with a basic background in linear algebra and engineering mathematics. Mastering the material in this textbook will prepare students to better understand, design, and implement fuzzy neural systems, develop new applications, and further advance the field.
There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images.
Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information. Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as: -Shannon information and information dynamics -neural complexity as an information processing system -memory and information storage in the interconnected neural web -extremum (maximum and minimum) information entropy -neural network training -non-conventional, statistical distance-measures for neural network optimizations -symmetric and asymmetric characteristics of information-theoretic error-metrics -algorithmic complexity based representation of neural information-theoretic parameters -genetic algorithms versus neural information -dynamics of neurocybernetics viewed in the information-theoretic plane -nonlinear, information-theoretic transfer function of the neural cellular units -statistical mechanics, neural networks, and information theory -semiotic framework of neural information processing and neural information flow -fuzzy information and neural networks -neural dynamics conceived through fuzzy information parameters -neural information flow dynamics -informatics of neural stochastic resonance Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying theconcepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.
With contributions from more than 40 renowned experts, Modeling in the Neurosciences: From Ionic Channels to Neural Networks is essential for those interested in neuronal modeling and quantitative neiroscience. Focusing on new mathematical and computer models, techniques and methods, this monograph represents a cohesive and comprehensive treatment of various aspects of the neurosciences from the biophysical, cellular and netwrok levels. Many state-of-the-art examples are presented as to how mathematical and computer modeling can contribute to the understanding of mechanisms and systems in the neurosciences. Each chapter also includes suggestions of possible refinements for future modeling in this rapidly changing and expanding field. This book will benefit and inspire the advanced modeler, and give the beginner sufficient confidence to model a wide selection of neuronal systems at the biophysical, cellular and network levels.
Mathematical psychology is an interdisciplinary area of research in
which methods of mathematics, operations research, and computer
science in psychology are used. Now more than thirty years old, the
field has continued to grow rapidly and has taken on a life of its
own. This volume summarizes recent progress in mathematical
psychology as seen by some of the leading figures in the field as
well as some of its leading young researchers.
This book contains the proceedings of the 22nd EANN "Engineering Applications of Neural Networks" 2021 that comprise of research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given in advances of machine learning (ML) focusing on the following algorithms-approaches: Augmented ML, autoencoders, adversarial neural networks, blockchain-adaptive methods, convolutional neural networks, deep learning, ensemble methods, learning-federated learning, neural networks, recurrent - long short-term memory. The application domains are related to: Anomaly detection, bio-medical AI, cyber-security, data fusion, e-learning, emotion recognition, environment, hyperspectral imaging, fraud detection, image analysis, inverse kinematics, machine vision, natural language, recommendation systems, robotics, sentiment analysis, simulation, stock market prediction.
Neural Network Analysis, Architectures and Applications discusses the main areas of neural networks, with each authoritative chapter covering the latest information from different perspectives. Divided into three parts, the book first lays the groundwork for understanding and simplifying networks. It then describes novel architectures and algorithms, including pulse-stream techniques, cellular neural networks, and multiversion neural computing. The book concludes by examining various neural network applications, such as neuron-fuzzy control systems and image compression. This final part of the book also provides a case study involving oil spill detection. This book is invaluable for students and practitioners who have a basic understanding of neural computing yet want to broaden and deepen their knowledge of the field.
Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
This superb new book is one of the first publications in recent years to provide a broad overview of this interdisciplinary field. Most of the book is written in a self contained manner, assuming only a general knowledge of statistical mechanics and basic probabilty theory . It provides the reader with a sound introduction to the field and to the analytical techniques necessary to follow its most recent developments
The addition of artificial neural network computing to traditional
pattern recognition has given rise to a new, different, and more
powerful methodology that is presented in this interesting book.
This is a practical guide to the application of artificial neural
networks.
Composed of three sections, this book presents the most popular
training algorithm for neural networks: backpropagation. The first
section presents the theory and principles behind backpropagation
as seen from different perspectives such as statistics, machine
learning, and dynamical systems. The second presents a number of
network architectures that may be designed to match the general
concepts of Parallel Distributed Processing with backpropagation
learning. Finally, the third section shows how these principles can
be applied to a number of different fields related to the cognitive
sciences, including control, speech recognition, robotics, image
processing, and cognitive psychology. The volume is designed to
provide both a solid theoretical foundation and a set of examples
that show the versatility of the concepts. Useful to experts in the
field, it should also be most helpful to students seeking to
understand the basic principles of connectionist learning and to
engineers wanting to add neural networks in general -- and
backpropagation in particular -- to their set of problem-solving
methods.
Composed of three sections, this book presents the most popular
training algorithm for neural networks: backpropagation. The first
section presents the theory and principles behind backpropagation
as seen from different perspectives such as statistics, machine
learning, and dynamical systems. The second presents a number of
network architectures that may be designed to match the general
concepts of Parallel Distributed Processing with backpropagation
learning. Finally, the third section shows how these principles can
be applied to a number of different fields related to the cognitive
sciences, including control, speech recognition, robotics, image
processing, and cognitive psychology. The volume is designed to
provide both a solid theoretical foundation and a set of examples
that show the versatility of the concepts. Useful to experts in the
field, it should also be most helpful to students seeking to
understand the basic principles of connectionist learning and to
engineers wanting to add neural networks in general -- and
backpropagation in particular -- to their set of problem-solving
methods.
Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.
Focuses on the definition, engineering, and delivery of AI solutions as opposed to AI itself Reader will still gain a strong understanding of AI, but through the perspective of delivering real solutions Explores the core AI issues that impact the success of an overall solution including i. realities of dealing with data, ii. impact of AI accuracy on the ability of the solution to meet business objectives, iii. challenges in managing the quality of machine learning models Includes real world examples of enterprise scale solutions Provides a series of (optional) technical deep dives and thought experiments.
This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.
This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.
The world is witnessing the rapid evolution of its own nervous
system by an unparalleled growth in communication technology. Like
the evolution of the nervous systems in animals, this growth is
being driven by a survival-of-the-fittest-mechanism. In
telecommunications, the entities that fuel this growth are
companies and nations who compete with each other. Companies with
superior information systems can outrun and outsmart others because
they serve their customers better.
It is necessary to practice methodological doubt, like Descartes, in - der to loosen the hold of mental habits; and it is necessary to cultivate logical imagination, in order to have a number of hypotheses at c- mand, and not to be the slave of the one which common sense has r- dered easy to imagine. These two processes, of doubting the familiar and imagining the unfamiliar, are corrective, and form the chief part of the mental training required for a philosopher. Bertrand Russell At every stage and in all circumstances knowledge is incomplete and provisional, conditioned and limited by the historical circumstances under which it was acquired, including the means and methods used for gaining it and the historically conditioned assumptions and categories used in the formulation of ideas and conclusions. Maurice Cornforth This monograph is the second in the series of meta-theoretic analysis of fuzzy paradigm and its contribution and possible contribution to formal reasoning in order to free the knowledge production process from the ridge frame of the classical paradigm that makes its application to soft and inexact sciences d- ficult or irrelevant. The work in the previous monograph was strictly devoted to problems of theory of knowledge and critique of classical, bounded and other rationalities in decision-choice processes regarding the principles of verification, falsification or corroboration in knowledge production. This monograph deals mostly with epistemic decision-choice models and theories and how they are related to both the classical and fuzzy paradigms.
Written for cognitive scientists, psychologists, computer
scientists, engineers, and neuroscientists, this book provides an
accessible overview of how computational network models are being
used to model neurobiological phenomena. Each chapter presents a
representative example of how biological data and network models
interact with the authors' research. The biological phenomena cover
network- or circuit-level phenomena in humans and other
higher-order vertebrates.
In the industry of manufacturing and design, one major constraint has been enhancing operating performance using less time. As technology continues to advance, manufacturers are looking for better methods in predicting the condition and residual lifetime of electronic devices in order to save repair costs and their reputation. Intelligent systems are a solution for predicting the reliability of these components; however, there is a lack of research on the advancements of this smart technology within the manufacturing industry. AI Techniques for Reliability Prediction for Electronic Components provides emerging research exploring the theoretical and practical aspects of prediction methods using artificial intelligence and machine learning in the manufacturing field. Featuring coverage on a broad range of topics such as data collection, fault tolerance, and health prognostics, this book is ideally designed for reliability engineers, electronic engineers, researchers, scientists, students, and faculty members seeking current research on the advancement of reliability analysis using AI. |
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