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Recurrent Neural Networks And Soft Computing
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Book Synopsis Recurrent Neural Networks and Soft Computing by : Mahmoud ElHefnawi
Download or read book Recurrent Neural Networks and Soft Computing written by Mahmoud ElHefnawi and published by BoD – Books on Demand. This book was released on 2012-03-30 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: New applications in recurrent neural networks are covered by this book, which will be required reading in the field. Methodological tools covered include ranking indices for fuzzy numbers, a neuro-fuzzy digital filter and mapping graphs of parallel programmes. The scope of the techniques profiled in real-world applications is evident from chapters on the recognition of severe weather patterns, adult and foetal ECGs in healthcare and the prediction of temperature time-series signals. Additional topics in this vein are the application of AI techniques to electromagnetic interference problems, bioprocess identification and I-term control and the use of BRNN-SVM to improve protein-domain prediction accuracy. Recurrent neural networks can also be used in virtual reality and nonlinear dynamical systems, as shown by two chapters.
Book Synopsis Recurrent Neural Networks by : Fathi M. Salem
Download or read book Recurrent Neural Networks written by Fathi M. Salem and published by Springer Nature. This book was released on 2022-01-03 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author’s approach enables strategic co-training of output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications.
Book Synopsis Theory, Concepts and Methods of Recurrent Neural Networks and Soft Computing by : Jeremy Rogerson
Download or read book Theory, Concepts and Methods of Recurrent Neural Networks and Soft Computing written by Jeremy Rogerson and published by . This book was released on 2015-02-18 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced information regarding the theory, concepts and applications of recurrent neural networks and the field of soft computing has been highlighted in this elaborative book. A broad spectrum of topics is encompassed in this book like neural networks and static modelling, neuro-fuzzy digital filter, ranking indices for fuzzy numbers, controller designs for nonlinear dynamic systems, etc. The aim of this book is to serve as a valuable source of reference for a wide range of readers including scientists, researchers and students. It consists of contributions made by veteran researchers from across the globe.
Book Synopsis Recurrent Neural Networks and Soft Computing by : Mahmoud ElHefnawi
Download or read book Recurrent Neural Networks and Soft Computing written by Mahmoud ElHefnawi and published by . This book was released on 2012 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: New applications in recurrent neural networks are covered by this book, which will be required reading in the field. Methodological tools covered include ranking indices for fuzzy numbers, a neuro-fuzzy digital filter and mapping graphs of parallel programmes. The scope of the techniques profiled in real-world applications is evident from chapters on the recognition of severe weather patterns, adult and foetal ECGs in healthcare and the prediction of temperature time-series signals. Additional topics in this vein are the application of AI techniques to electromagnetic interference problems, bioprocess identification and I-term control and the use of BRNN-SVM to improve protein-domain prediction accuracy. Recurrent neural networks can also be used in virtual reality and nonlinear dynamical systems, as shown by two chapters.
Book Synopsis Convergence Analysis of Recurrent Neural Networks by : Zhang Yi
Download or read book Convergence Analysis of Recurrent Neural Networks written by Zhang Yi and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the outstanding and pioneering research work of Hopfield on recurrent neural networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong interests in scientists and researchers. Recent years have recorded a remarkable advance in research and development work on RNNs, both in theoretical research as weIl as actual applications. The field of RNNs is now transforming into a complete and independent subject. From theory to application, from software to hardware, new and exciting results are emerging day after day, reflecting the keen interest RNNs have instilled in everyone, from researchers to practitioners. RNNs contain feedback connections among the neurons, a phenomenon which has led rather naturally to RNNs being regarded as dynamical systems. RNNs can be described by continuous time differential systems, discrete time systems, or functional differential systems, and more generally, in terms of non linear systems. Thus, RNNs have to their disposal, a huge set of mathematical tools relating to dynamical system theory which has tumed out to be very useful in enabling a rigorous analysis of RNNs.
Book Synopsis Neural Systems for Control by : Omid Omidvar
Download or read book Neural Systems for Control written by Omid Omidvar and published by Elsevier. This book was released on 1997-02-24 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory Represents the most up-to-date developments in this rapidly growing application area of neural networks Takes a new and novel approach to system identification and synthesis
Book Synopsis Handbook on Neural Information Processing by : Monica Bianchini
Download or read book Handbook on Neural Information Processing written by Monica Bianchini and published by Springer Science & Business Media. This book was released on 2013-04-12 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Recurrent Neural Networks by : Larry Medsker
Download or read book Recurrent Neural Networks written by Larry Medsker and published by CRC Press. This book was released on 1999-12-20 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks. This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects. The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.
Book Synopsis Neural Information Processing: Research and Development by : Jagath Chandana Rajapakse
Download or read book Neural Information Processing: Research and Development written by Jagath Chandana Rajapakse and published by Springer. This book was released on 2012-12-06 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of neural information processing has two main objects: investigation into the functioning of biological neural networks and use of artificial neural networks to sol ve real world problems. Even before the reincarnation of the field of artificial neural networks in mid nineteen eighties, researchers have attempted to explore the engineering of human brain function. After the reincarnation, we have seen an emergence of a large number of neural network models and their successful applications to solve real world problems. This volume presents a collection of recent research and developments in the field of neural information processing. The book is organized in three Parts, i.e., (1) architectures, (2) learning algorithms, and (3) applications. Artificial neural networks consist of simple processing elements called neurons, which are connected by weights. The number of neurons and how they are connected to each other defines the architecture of a particular neural network. Part 1 of the book has nine chapters, demonstrating some of recent neural network architectures derived either to mimic aspects of human brain function or applied in some real world problems. Muresan provides a simple neural network model, based on spiking neurons that make use of shunting inhibition, which is capable of resisting small scale changes of stimulus. Hoshino and Zheng simulate a neural network of the auditory cortex to investigate neural basis for encoding and perception of vowel sounds.
Book Synopsis Recurrent Neural Networks by : Amit Kumar Tyagi
Download or read book Recurrent Neural Networks written by Amit Kumar Tyagi and published by CRC Press. This book was released on 2022-08-08 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding. FEATURES Covers computational analysis and understanding of natural languages Discusses applications of recurrent neural network in e-Healthcare Provides case studies in every chapter with respect to real-world scenarios Examines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logistics The text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.
Book Synopsis Recurrent Neural Networks with Python Quick Start Guide by : Simeon Kostadinov
Download or read book Recurrent Neural Networks with Python Quick Start Guide written by Simeon Kostadinov and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long short-term memory unitsExpand your skills in complex neural network and deep learning topicsBook Description Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is for This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.
Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal
Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Book Synopsis Recurrent Neural Networks by : Fouad Sabry
Download or read book Recurrent Neural Networks written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-26 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Recurrent Neural Networks An artificial neural network that belongs to the class known as recurrent neural networks (RNNs) is one in which the connections between its nodes can form a cycle. This allows the output of some nodes to have an effect on subsequent input to the very same nodes. Because of this, it is able to display temporally dynamic behavior. RNNs are a descendant of feedforward neural networks and have the ability to use their internal state (memory) to process input sequences of varying lengths. Because of this, they are suitable for applications such as speech recognition and unsegmented, connected handwriting recognition. Theoretically, recurrent neural networks are considered to be Turing complete since they are able to execute arbitrary algorithms and interpret arbitrary sequences of inputs. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Recurrent neural network Chapter 2: Artificial neural network Chapter 3: Backpropagation Chapter 4: Long short-term memory Chapter 5: Types of artificial neural networks Chapter 6: Deep learning Chapter 7: Vanishing gradient problem Chapter 8: Bidirectional recurrent neural networks Chapter 9: Gated recurrent unit Chapter 10: Attention (machine learning) (II) Answering the public top questions about recurrent neural networks. (III) Real world examples for the usage of recurrent neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of recurrent neural networks. What Is Artificial Intelligence Series The Artificial Intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Book Synopsis Soft Computing and Its Applications by : Rafik Aziz ogly Aliev
Download or read book Soft Computing and Its Applications written by Rafik Aziz ogly Aliev and published by World Scientific. This book was released on 2001 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of soft computing is still in its initial stages of crystallization. Presently available books on soft computing are merely collections of chapters or articles about different aspects of the field. This book is the first to provide a systematic account of the major concepts and methodologies of soft computing, presenting a unified framework that makes the subject more accessible to students and practitioners. Particularly worthy of note is the inclusion of a wealth of information about neuro-fuzzy, neuro-genetic, fuzzy-genetic and neuro-fuzzy-genetic systems, with many illuminating applications and examples.
Book Synopsis Knowledge-Based Neurocomputing: A Fuzzy Logic Approach by : Eyal Kolman
Download or read book Knowledge-Based Neurocomputing: A Fuzzy Logic Approach written by Eyal Kolman and published by Springer. This book was released on 2008-10-18 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this monograph, the authors introduce a novel fuzzy rule-base, referred to as the Fuzzy All-permutations Rule-Base (FARB). They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network. Conversely, every standard artificial neural network has an equivalent FARB. The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks.
Book Synopsis Neural Networks in a Softcomputing Framework by : Ke-Lin Du
Download or read book Neural Networks in a Softcomputing Framework written by Ke-Lin Du and published by Springer Science & Business Media. This book was released on 2006-08-02 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: This concise but comprehensive textbook reviews the most popular neural-network methods and their associated techniques. Each chapter provides state-of-the-art descriptions of important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms – powerful tools for neural-network learning – are introduced. The systematic survey of neural-network models and exhaustive references list will point readers toward topics for future research. The algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.
Book Synopsis Soft Computing for Problem Solving by : Jagdish Chand Bansal
Download or read book Soft Computing for Problem Solving written by Jagdish Chand Bansal and published by Springer. This book was released on 2018-10-30 with total page 991 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume book presents outcomes of the 7th International Conference on Soft Computing for Problem Solving, SocProS 2017. This conference is a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), the Indian Institute of Technology Roorkee, the South Asian University New Delhi and the National Institute of Technology Silchar, and brings together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions The book presents the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers in the areas including, but not limited to, algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It is a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.
