Neural Network Modeling

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Neural Network Models

Author : Philippe de Wilde
ISBN : 3540761292
Genre : Technology & Engineering
File Size : 49.6 MB
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Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. It presents the essential principles of nonlinear dynamics as derived from neurobiology, and investigates the stability, convergence behaviour and capacity of networks.
Category: Technology & Engineering

Semi Empirical Neural Network Modeling And Digital Twins Development

Author : Dmitriy Tarkhov
ISBN : 9780128156520
Genre : Science
File Size : 64.27 MB
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Semi-empirical Neural Network Modeling presents a new approach on how to quickly construct an accurate, multilayered neural network solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single-layered neural networks built on the finite element method (FEM). The strength of the new method presented in this book is the automatic inclusion of task parameters in the final solution formula, which eliminates the need for repeated problem-solving. This is especially important for constructing individual models with unique features. The book illustrates key concepts through a large number of specific problems, both hypothetical models and practical interest. Offers a new approach to neural networks using a unified simulation model at all stages of design and operation Illustrates this new approach with numerous concrete examples throughout the book Presents the methodology in separate and clearly-defined stages
Category: Science

A Comprehensive Guide To Neural Network Modeling

Author : Steffen Skaar
ISBN : 1536185426
Genre : Computers
File Size : 79.4 MB
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As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications of artificial neural networks in food engineering are presented, particularly focusing on control, monitoring and modeling of industrial food processes.The authors emphasize the main achievements of artificial neural network modeling in recent years in the field of quantitative structure-activity relationships and quantitative structure-retention relationships.In the closing study, artificial intelligence techniques are applied to river water quality data and artificial intelligence models are developed in an effort to contribute to the reduction of the cost of future on-line measurement stations.
Category: Computers

Neural Network Modeling

Author : P. S. Neelakanta
ISBN : 9781351428958
Genre : Technology & Engineering
File Size : 46.26 MB
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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.
Category: Technology & Engineering

Artificial Neural Network Modelling

Author : Subana Shanmuganathan
ISBN : 9783319284958
Genre : Computers
File Size : 88.87 MB
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This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.
Category: Computers

Neural Network Modeling Using Sas Enterprise Miner

Author : Randall Matignon
ISBN : 9781418423414
Genre : Computers
File Size : 73.58 MB
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This book is designed in making statisticians, researchers, and programmers aware of the awesome new product now available in SAS called Enterprise Miner. The book will also make readers get familiar with the neural network forecasting methodology in statistics. One of the goals to this book is making the powerful new SAS module called Enterprise Miner easy for you to use with step-by-step instructions in creating a Enterprise Miner process flow diagram in preparation to data-mining analysis and neural network forecast modeling. Topics discussed in this book An overview to traditional regression modeling. An overview to neural network modeling. Numerical examples of various neural network designs and optimization techniques. An overview to the powerful SAS product called Enterprise Miner. An overview to the SAS neural network modeling procedure called PROC NEURAL. Designing a SAS Enterprise Miner process flow diagram to perform neural network forecast modeling and traditional regression modeling with an explanation to the various configuration settings to the Enterprise Miner nodes used in the analysis. Comparing neural network forecast modeling estimates with traditional modeling estimates based on various examples from SAS manuals and literature with an added overview to the various modeling designs and a brief explanation to the SAS modeling procedures, option statements, and corresponding SAS output listings.
Category: Computers

Neural Network Modeling And Identification Of Dynamical Systems

Author : Yury Tiumentsev
ISBN : 9780128154304
Genre : Science
File Size : 33.69 MB
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Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training Offers application examples of dynamic neural network technologies, primarily related to aircraft Provides an overview of recent achievements and future needs in this area
Category: Science

Physical Models Of Neural Networks

Author : Tam s Geszti
ISBN : 9810200129
Genre : Computers
File Size : 50.70 MB
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This lecture note volume is mainly about the recent development that connected neural network modeling to the theoretical physics of disordered systems. It gives a detailed account of the (Little-) Hopfield model and its ramifications concerning non-orthogonal and hierarchical patterns, short-term memory, time sequences, and dynamical learning algorithms. It also offers a brief introduction to computation in layered feed-forward networks, trained by back-propagation and other methods. Kohonen's self-organizing feature map algorithm is discussed in detail as a physical ordering process. The book offers a minimum complexity guide through the often cumbersome theories developed around the Hopfield model. The physical model for the Kohonen self-organizing feature map algorithm is new, enabling the reader to better understand how and why this fascinating and somewhat mysterious tool works.
Category: Computers

Fundamentals Of Neural Network Modeling

Author : Randolph W. Parks
ISBN : 0262161753
Genre : Science
File Size : 48.96 MB
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Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics. The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease. Contributors J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble
Category: Science

Motivation Effort And The Neural Network Model

Author : Theodore Wasserman
ISBN : 3030587231
Genre : Psychology
File Size : 27.61 MB
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Our understanding of how the human brain operates and completes its essential tasks continues is fundamentally altered from what it was ten years ago. We have moved from an understanding based on the modularity of key structural components and their specialized functions to an almost diametrically opposed, highly integrated neural network model, based on a vertically organized brain dependent on small world hub principles. This new understanding completely changes how we understand essential psychological constructs such as motivation. Network modeling posits that motivation is a construct that describes a modified aspect of the operation of the human learning system that is specifically designed to cause a person to pursue a goal. Anthropologically and developmentally, these goals were initially basic, including things like food, shelter and reproduction. Over the course of time and development they develop into a complex web of extrinsic and then intrinsic goals, objectives and values. The core for all of this development is the inborn flight or fight reaction has been modified over time by a combination of inborn human temperamental characteristics and life experiences. This process of modification is, in part, based on the operation of a network based error-prediction network working in concert with the reward network to produce a system of ever evolving valuations of goals and objectives. These valuations are never truly fixed. They are constantly evolving, being modified and shaped by experience. The error prediction network and learning related networks work in concert with the limbic system to allow affect laden experiences to inform the process of valuation. These networks, operating in concert, produce a cognitive process we call motivation. Like most networks, the motivation system of networks is recruited when the task demands of the situation require them. Understanding motivation from this perspective has profound implications for many scientific disciplines in general and psychology in specific. Psychologically, this new understanding will alter how we understand client behavior in therapy and when being evaluated. This new understanding will provide direction for new therapeutic intervention for a variety of disorders of mental health. It will also inform testing practices concerning the evaluation of effort and malingering. This book is not a project in reductionism. It is the polar opposite. A neural network understanding of the operation of the human brain allows for the integration of what has come before into a comprehensive and integrated model. It will likely provide the basis for future research for years to come.
Category: Psychology

Neural Network Models Of Cognition

Author : John W. Donahoe
ISBN : 0444819312
Genre : Computers
File Size : 77.16 MB
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This internationally authored volume presents major findings, concepts, and methods of behavioral neuroscience coordinated with their simulation via neural networks. A central theme is that biobehaviorally constrained simulations provide a rigorous means to explore the implications of relatively simple processes for the understanding of cognition (complex behavior). Neural networks are held to serve the same function for behavioral neuroscience as population genetics for evolutionary science. The volume is divided into six sections, each of which includes both experimental and simulation research: (1) neurodevelopment and genetic algorithms, (2) synaptic plasticity (LTP), (3) sensory/hippocampal systems, (4) motor systems, (5) plasticity in large neural systems (reinforcement learning), and (6) neural imaging and language. The volume also includes an integrated reference section and a comprehensive index.
Category: Computers

Deep Learning Essentials

Author : Anurag Bhardwaj
ISBN : 9781785887772
Genre : Computers
File Size : 66.81 MB
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Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.
Category: Computers

Gateway To Memory

Author : Mark A. Gluck
ISBN : 0262571528
Genre : Medical
File Size : 65.11 MB
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This book is for students and researchers who have a specific interest in learning and memory and want to understand how computational models can be integrated into experimental research on the hippocampus and learning. It emphasizes the function of brain structures as they give rise to behavior, rather than the molecular or neuronal details. It also emphasizes the process of modeling, rather than the mathematical details of the models themselves. The book is divided into two parts. The first part provides a tutorial introduction to topics in neuroscience, the psychology of learning and memory, and the theory of neural network models. The second part, the core of the book, reviews computational models of how the hippocampus cooperates with other brain structures--including the entorhinal cortex, basal forebrain, cerebellum, and primary sensory and motor cortices--to support learning and memory in both animals and humans. The book assumes no prior knowledge of computational modeling or mathematics. For those who wish to delve more deeply into the formal details of the models, there are optional "mathboxes" and appendices. The book also includes extensive references and suggestions for further readings.
Category: Medical

Neural Network Modeling Of The Head Related Transfer Function

Author : Damion Reinhardt
ISBN : 1423564464
Genre : Directional hearing
File Size : 57.37 MB
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Battlefield synthesis of 3-D audio may require the interpolation and compression of head-related transfer function (HRTF) data. This thesis is an implementation of a functional model of the HRTF using artificial neural networks (ANNs), the model provides both compression and interpolation.
Category: Directional hearing

Neural Network Modeling

Author : P. S. Neelakanta
ISBN : 9781351428965
Genre : Technology & Engineering
File Size : 29.95 MB
Format : PDF
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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.
Category: Technology & Engineering

Neural Network Modeling

Author : William J. E. Potts
ISBN : 1590477715
Genre : Neural networks (Computer science)
File Size : 38.66 MB
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Category: Neural networks (Computer science)

Wavelet Neural Networks

Author : Antonios K. Alexandridis
ISBN : 9781118592526
Genre : Mathematics
File Size : 37.93 MB
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A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification. The authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification also includes: • Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence • Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction • An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks • Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics.
Category: Mathematics