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Author : Victor Manuel Landassuri Moreno
Publisher :
ISBN 13 :
Total Pages : 204 pages
Book Rating : 4.:/5 (911 download)
Download or read book Evolution of Modular Neural Networks written by Victor Manuel Landassuri Moreno and published by . This book was released on 2012 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jacob M.J. Murre
Publisher : Psychology Press
ISBN 13 : 1317781376
Total Pages : 257 pages
Book Rating : 4.3/5 (177 download)
Download or read book Learning and Categorization in Modular Neural Networks written by Jacob M.J. Murre and published by Psychology Press. This book was released on 2014-02-25 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new neural network model called CALM, for categorization and learning in neural networks. The author demonstrates how this model can learn the word superiority effect for letter recognition, and discusses a series of studies that simulate experiments in implicit and explicit memory, involving normal and amnesic patients. Pathological, but psychologically accurate, behavior is produced by "lesioning" the arousal system of these models. A concise introduction to genetic algorithms, a new computing method based on the biological metaphor of evolution, and a demonstration on how these algorithms can design network architectures with superior performance are included in this volume. The role of modularity in parallel hardware and software implementations is considered, including transputer networks and a dedicated 400-processor neurocomputer built by the developers of CALM in cooperation with Delft Technical University. Concluding with an evaluation of the psychological and biological plausibility of CALM models, the book offers a general discussion of catastrophic interference, generalization, and representational capacity of modular neural networks. Researchers in cognitive science, neuroscience, computer simulation sciences, parallel computer architectures, and pattern recognition will be interested in this volume, as well as anyone engaged in the study of neural networks, neurocomputers, and neurosimulators.
Download or read book The Evolution of Modular Artificial Neural Networks written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming and Genetic Programming; however, these often fail in the evolution of complex systems, particularly when such systems involve multi-domain sensory information which interacts in complex ways with system outputs. The aim in this work is to produce an evolutionary method that allows the structure of the network to evolve from simple to complex as it interacts with a dynamic environment. This new algorithm is therefore based on Incremental Evolution. A simulated model of a legged robot was used as a test-bed for the approach. The algorithm starts with a simple robotic body plan. This then grows incrementally in complexity along with its controlling neural network and the environment it reacts with. The network grows by adding modules to its structure - so the technique may also be termed a Growth Algorithm. Experiments are presented showing the successful evolution of multi-legged gaits and a simple vision system. These are then integrated together to form a complete robotic system. The possibility of the evolution of complex systems is one advantage of the algorithm and it is argued that it represents a possible path towards more advanced artificial intelligence. Applications in Electronics, Computer Science, Mechanical Engineering and Aerospace are also discussed.
Author : Daniela Sanchez
Publisher : Springer
ISBN 13 : 3319288628
Total Pages : 107 pages
Book Rating : 4.3/5 (192 download)
Download or read book Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation written by Daniela Sanchez and published by Springer. This book was released on 2016-02-23 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.
Author : Vineet R. Khare
Publisher :
ISBN 13 :
Total Pages : 152 pages
Book Rating : 4.:/5 (911 download)
Download or read book Atomatic Problem Decomposition Using Co-evolution and Modular Neural Networks written by Vineet R. Khare and published by . This book was released on 2006 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Anupam Shukla
Publisher : Springer Science & Business Media
ISBN 13 : 3642143431
Total Pages : 467 pages
Book Rating : 4.6/5 (421 download)
Download or read book Towards Hybrid and Adaptive Computing written by Anupam Shukla and published by Springer Science & Business Media. This book was released on 2010-08-17 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soft Computing today is a very vast field whose extent is beyond measure. This book offers a well structured presentation of the basic concepts of Artificial Neural Networks, Fuzzy Inference Systems and Evolutionary Algorithms.
Author : Jacob Murre
Publisher : Psychology Press
ISBN 13 : 9780805813388
Total Pages : 244 pages
Book Rating : 4.8/5 (133 download)
Download or read book Learning and Categorization in Modular Neural Networks written by Jacob Murre and published by Psychology Press. This book was released on 1992 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new neural network model called CALM, for categorization and learning in neural networks. The author demonstrates how this model can learn the word superiority effect for letter recognition, and discusses a series of studies that simulate experiments in implicit and explicit memory, involving normal and amnesic patients. Pathological, but psychologically accurate, behavior is produced by "lesioning" the arousal system of these models. A concise introduction to genetic algorithms, a new computing method based on the biological metaphor of evolution, and a demonstration on how these algorithms can design network architectures with superior performance are included in this volume. The role of modularity in parallel hardware and software implementations is considered, including transputer networks and a dedicated 400-processor neurocomputer built by the developers of CALM in cooperation with Delft Technical University. Concluding with an evaluation of the psychological and biological plausibility of CALM models, the book offers a general discussion of catastrophic interference, generalization, and representational capacity of modular neural networks. Researchers in cognitive science, neuroscience, computer simulation sciences, parallel computer architectures, and pattern recognition will be interested in this volume, as well as anyone engaged in the study of neural networks, neurocomputers, and neurosimulators.
Author : Patricia Melin
Publisher : Springer Science & Business Media
ISBN 13 : 3642241387
Total Pages : 216 pages
Book Rating : 4.6/5 (422 download)
Download or read book Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition written by Patricia Melin and published by Springer Science & Business Media. This book was released on 2011-10-18 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.
Author : Vassilios Petridis
Publisher : Springer Science & Business Media
ISBN 13 : 1461555558
Total Pages : 311 pages
Book Rating : 4.4/5 (615 download)
Download or read book Predictive Modular Neural Networks written by Vassilios Petridis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.
Author : Werner Callebaut
Publisher : MIT Press
ISBN 13 : 9780262033268
Total Pages : 480 pages
Book Rating : 4.0/5 (332 download)
Download or read book Modularity written by Werner Callebaut and published by MIT Press. This book was released on 2005 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modularity—the attempt to understand systems as integrations of partially independent and interacting units—is today a dominant theme in the life sciences, cognitive science, and computer science. The concept goes back at least implicitly to the Scientific (or Copernican) Revolution, and can be found behind later theories of phrenology, physiology, and genetics; moreover, art, engineering, and mathematics rely on modular design principles. This collection broadens the scientific discussion of modularity by bringing together experts from a variety of disciplines, including artificial life, cognitive science, economics, evolutionary computation, developmental and evolutionary biology, linguistics, mathematics, morphology, paleontology, physics, theoretical chemistry, philosophy, and the arts. The contributors debate and compare the uses of modularity, discussing the different disciplinary contexts of "modular thinking" in general (including hierarchical organization, near-decomposability, quasi-independence, and recursion) or of more specialized concepts (including character complex, gene family, encapsulation, and mosaic evolution); what modules are, why and how they develop and evolve, and the implication for the research agenda in the disciplines involved; and how to bring about useful cross-disciplinary knowledge transfer on the topic. The book includes a foreword by the late Herbert A. Simon addressing the role of near-decomposability in understanding complex systems. Contributors: Lee Altenberg, Lauren W. Ancel-Meyers, Carl Anderson, Robert B. Brandon, Angela D. Buscalioni, Raffaele Calabretta, Werner Callebaut, Anne De Joan, Rafael Delgado-Buscalioni, Gunther J. Eble, Walter Fontana, Fernand Gobet, Alicia de la Iglesia, Slavik V. Jablan, Luigi Marengo, Daniel W. McShea, Jason Mezey, D. Kimbrough Oller, Domenico Parisi, Corrado Pasquali, Diego Rasskin-Gutman, Gerhard Schlosser, Herbert A. Simon, Roger D. K. Thomas, Marco Valente, Boris M. Velichkovsky, Gunter P. Wagner, Rasmus G. Winter Vienna Series in Theoretical Biology
Author : Seyedali Mirjalili
Publisher : Springer Nature
ISBN 13 : 9813299908
Total Pages : 286 pages
Book Rating : 4.8/5 (132 download)
Download or read book Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and published by Springer Nature. This book was released on 2019-11-11 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
Author : Andrej Dobnikar
Publisher : Springer Science & Business Media
ISBN 13 : 3709163846
Total Pages : 365 pages
Book Rating : 4.7/5 (91 download)
Download or read book Artificial Neural Nets and Genetic Algorithms written by Andrej Dobnikar and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the contents: Neural networks – theory and applications: NNs (= neural networks) classifier on continuous data domains– quantum associative memory – a new class of neuron-like discrete filters to image processing – modular NNs for improving generalisation properties – presynaptic inhibition modelling for image processing application – NN recognition system for a curvature primal sketch – NN based nonlinear temporal-spatial noise rejection system – relaxation rate for improving Hopfield network – Oja's NN and influence of the learning gain on its dynamics Genetic algorithms – theory and applications: transposition: a biological-inspired mechanism to use with GAs (= genetic algorithms) – GA for decision tree induction – optimising decision classifications using GAs – scheduling tasks with intertask communication onto multiprocessors by GAs – design of robust networks with GA – effect of degenerate coding on GAs – multiple traffic signal control using a GA – evolving musical harmonisation – niched-penalty approach for constraint handling in GAs – GA with dynamic population size – GA with dynamic niche clustering for multimodal function optimisation Soft computing and uncertainty: self-adaptation of evolutionary constructed decision trees by information spreading – evolutionary programming of near optimal NNs
Author : Victor Hannak
Publisher :
ISBN 13 :
Total Pages : 150 pages
Book Rating : 4.:/5 (565 download)
Download or read book Concurrent Evolution of Feature Extractors and Modular Artificial Neural Networks written by Victor Hannak and published by . This book was released on 2004 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: A novel method for evolving customized artificial neural networks with correlated feature extractors was designed and tested, involving the use of concurrent evolutionary processes as a mechanism to search the space of recognition networks.
Author : Colin R. Tosh
Publisher : Cambridge University Press
ISBN 13 : 1139489054
Total Pages : 409 pages
Book Rating : 4.1/5 (394 download)
Download or read book Modelling Perception with Artificial Neural Networks written by Colin R. Tosh and published by Cambridge University Press. This book was released on 2010-06-24 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Studies of the evolution of animal signals and sensory behaviour have more recently shifted from considering 'extrinsic' (environmental) determinants to 'intrinsic' (physiological) ones. The drive behind this change has been the increasing availability of neural network models. With contributions from experts in the field, this book provides a complete survey of artificial neural networks. The book opens with two broad, introductory level reviews on the themes of the book: neural networks as tools to explore the nature of perceptual mechanisms, and neural networks as models of perception in ecology and evolutionary biology. Later chapters expand on these themes and address important methodological issues when applying artificial neural networks to study perception. The final chapter provides perspective by introducing a neural processing system in a real animal. The book provides the foundations for implementing artificial neural networks, for those new to the field, along with identifying potential research areas for specialists.
Author : Oscar Castillo
Publisher : Springer Science & Business Media
ISBN 13 : 3642045138
Total Pages : 320 pages
Book Rating : 4.6/5 (42 download)
Download or read book Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control written by Oscar Castillo and published by Springer Science & Business Media. This book was released on 2009-10-09 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: We describe in this book, new methods for evolutionary design of intelligent s- tems using soft computing and their applications in modeling, simulation and c- trol. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and evolutionary algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in four main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of evolutionary design of fuzzy systems in intelligent control, which consists of papers that propose new methods for designing and optimizing intelligent controllers for different applications. The second part c- tains papers with the main theme of evolutionary design of intelligent systems for pattern recognition applications, which are basically papers using evolutionary al- rithms for optimizing modular neural networks with fuzzy systems for response - tegration, for achieving pattern recognition in different applications. The third part contains papers with the themes of models for learning and social simulation, which are papers that apply intelligent systems to the problems of designing learning - jects and social agents. The fourth part contains papers that deal with intelligent s- tems in robotics applications and hardware implementations. In the part of Intelligent Control there are 5 papers that describe different c- tributions on evolutionary optimization of fuzzy systems in intelligent control. The first paper, by Ricardo Martinez-Marroquin et al.
Author : Tomas Hrycej
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 264 pages
Book Rating : 4.3/5 (91 download)
Download or read book Modular Learning in Neural Networks written by Tomas Hrycej and published by Wiley-Interscience. This book was released on 1992-10-09 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Modular Learning in Neural Networks covers the full range of conceivable approaches to the modularization of learning, including decomposition of learning into modules using supervised and unsupervised learning types; decomposition of the function to be mapped into linear and nonlinear parts; decomposition of the neural network to minimize harmful interferences between a large number of network parameters during learning; decomposition of the application task into subtasks that are learned separately; decomposition into a knowledge-based part and a learning part. The book attempts to show that modular learning based on these approaches is helpful in improving the learning performance of neural networks. It demonstrates this by applying modular methods to a pair of benchmark cases - a medical classification problem of realistic size, encompassing 7,200 cases of thyroid disorder; and a handwritten digits classification problem, involving several thousand cases. In so doing, the book shows that some of the proposed methods lead to substantial improvements in solution quality and learning speed, as well as enhanced robustness with regard to learning control parameters.".
Author : Mukesh Patel
Publisher : MIT Press
ISBN 13 : 9780262162012
Total Pages : 510 pages
Book Rating : 4.1/5 (62 download)
Download or read book Advances in the Evolutionary Synthesis of Intelligent Agents written by Mukesh Patel and published by MIT Press. This book was released on 2001 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning. Among the first uses of the computer was the development of programs to model perception, reasoning, learning, and evolution. Further developments resulted in computers and programs that exhibit aspects of intelligent behavior. The field of artificial intelligence is based on the premise that thought processes can be computationally modeled. Computational molecular biology brought a similar approach to the study of living systems. In both cases, hypotheses concerning the structure, function, and evolution of cognitive systems (natural as well as synthetic) take the form of computer programs that store, organize, manipulate, and use information. Systems whose information processing structures are fully programmed are difficult to design for all but the simplest applications. Real-world environments call for systems that are able to modify their behavior by changing their information processing structures. Cognitive and information structures and processes, embodied in living systems, display many effective designs for biological intelligent agents. They are also a source of ideas for designing artificial intelligent agents. This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning. The book is organized around four topics: the power of evolution to determine effective solutions to complex tasks, mechanisms to make evolutionary design scalable, the use of evolutionary search in conjunction with local learning algorithms, and the extension of evolutionary search in novel directions.
