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Recurrent Neural Networks Concepts and Applications

Recurrent Neural Networks Concepts and Applications

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. | Recurrent Neural Networks Concepts and Applications

GBP 150.00
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Artificial Neural Networks in Biological and Environmental Analysis

Artificial Neural Networks in Biological and Environmental Analysis

Originating from models of biological neural systems artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability and made widely accessible with the co-evolution of software algorithms and methodologies artificial neural networks have had a profound impact in the elucidation of complex biological chemical and environmental processes. Artificial Neural Networks in Biological and Environmental Analysis provides an in-depth and timely perspective on the fundamental technological and applied aspects of computational neural networks. Presenting the basic principles of neural networks together with applications in the field the book stimulates communication and partnership among scientists in fields as diverse as biology chemistry mathematics medicine and environmental science. This interdisciplinary discourse is essential not only for the success of independent and collaborative research and teaching programs but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers: A brief history of computational neural network models in relation to brain function Neural network operations including neuron connectivity and layer arrangement Basic building blocks of model design selection and application from a statistical perspective Neurofuzzy systems neuro-genetic systems and neuro-fuzzy-genetic systems Function of neural networks in the study of complex natural processes Scientists deal with very complicated systems much of the inner workings of which are frequently unknown to researchers. Using only simple linear mathematical methods information that is needed to truly understand natural systems may be lost. The development of new algorithms to model such processes is needed and ANNs can play a major role. Balancing basic principles and diverse applications this text introduces newcomers to the field and reviews recent developments of interest to active neural network practitioners.

GBP 69.99
1

Discrete-Time Recurrent Neural Control Analysis and Applications

Discrete-Time Recurrent Neural Control Analysis and Applications

The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses based on the Lyapunov approach to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme. This book on Discrete-time Recurrent Neural Control is unique in the literature with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems. The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market. It is an excellent book after all. — Guanrong Chen City University of Hong Kong This book includes very relevant topics about neural control. In these days Artificial Neural Networks have been recovering their relevance and well-stablished importance this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks however most of the developments in this research area only focuses on applicability of the proposed schemes. However Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However its applications are not limited to these kind of systems due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author. — Alma Y. Alanis University of Guadalajara MexicoThis book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors boost converters inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones. — Rajesh Joseph Abraham Indian Institute of Space Science & Technology Thiruvananthapuram India | Discrete-Time Recurrent Neural Control Analysis and Applications

GBP 66.99
1

Binary Neural Networks Algorithms Architectures and Applications

Binary Neural Networks Algorithms Architectures and Applications

Deep learning has achieved impressive results in image classification computer vision and natural language processing. To achieve better performance deeper and wider networks have been designed which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context Binary Neural Networks: Algorithms Architectures and Applications will focus on CNN compression and acceleration which are important for the research community. We will describe numerous methods including parameter quantization network pruning low-rank decomposition and knowledge distillation. More recently to reduce the burden of handcrafted architecture design neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification speech recognition object detection and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book. Key Features Reviews recent advances in CNN compression and acceleration Elaborates recent advances on binary neural network (BNN) technologies Introduces applications of BNN in image classification speech recognition object detection and more | Binary Neural Networks Algorithms Architectures and Applications

GBP 110.00
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Convolutional Neural Networks in Visual Computing A Concise Guide

Convolutional Neural Networks in Visual Computing A Concise Guide

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. | Convolutional Neural Networks in Visual Computing A Concise Guide

GBP 69.99
1

Artificial Neural Networks in Cancer Diagnosis Prognosis and Patient Management

Artificial Neural Networks in Cancer Diagnosis Prognosis and Patient Management

The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to receive increased recognition. Research and case studies can be found scattered throughout a multitude of journals. Artificial Neural Networks in Cancer Diagnosis Prognosis and Patient Management brings together the work of top researchers - primarily clinicians - who present the results of their state-of-the-art work with ANNs as applied to nearly all major areas of cancer for diagnosis prognosis and management of the disease. The book introduces the theory of neural networks and the method of their application in oncology. It is not an exercise in ANN research but the presentation of a new technique for diagnosing and determining the treatment of cancers. The authors have included almost all cancers for which there exist ANN applications. When the data available is ill-defined and the development of an algorithmic solution difficult neural networks provide a non-linear approach which helps sift through the maze of information and arrive at a reasonable solution. Highly interdisciplinary in nature this book provides comprehensive coverage of the most important materials relating to the applications of ANNs in the cancer field. With contributions from prominent research centers worldwide it serves as an introduction to how neural networks can be used for accurate prediction or diagnosis and shows why neural networks are more accurate. Artificial Neural Networks in Cancer Diagnosis Prognosis and Patient Management gives you an understanding of this new tool its applications and when it should be used.

GBP 56.99
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Gas Turbines Modeling Simulation and Control Using Artificial Neural Networks

Gas Turbines Modeling Simulation and Control Using Artificial Neural Networks

Gas Turbines Modeling Simulation and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling simulation and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification the book: Outlines important criteria to consider at the beginning of the GT modeling process such as GT types and configurations control system types and configurations and modeling methods and objectives Highlights research in the fields of white-box and black-box modeling simulation and control of GTs exploring models of low-power GTs industrial power plant gas turbines (IPGTs) and aero GTs Discusses the structure of ANNs and the ANN-based model-building process including system analysis data acquisition and preparation network architecture and network training and validation Presents a noteworthy ANN-based methodology for offline system identification of GTs complete with validated models using both simulated and real operational data Covers the modeling of GT transient behavior and start-up operation and the design of proportional-integral-derivative (PID) and neural network-based controllers Gas Turbines Modeling Simulation and Control: Using Artificial Neural Networks not only offers a comprehensive review of the state of the art of gas turbine modeling and intelligent techniques but also demonstrates how artificial intelligence can be used to solve complicated industrial problems specifically in the area of GTs. | Gas Turbines Modeling Simulation and Control Using Artificial Neural Networks

GBP 69.99
1

Deep Neural Network Applications

Deep Neural Network Applications

The world is on the verge of fully ushering in the fourth industrial revolution of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machineries in the industries during the first industrial revolution; the internal combustion engine that gave rise to cars trucks and airplanes; electricity that caused the second industrial revolution through the discovery of direct and alternating current; and the Internet which led to the emergence of the information age AI is a transformational technology. It will cause a paradigm shift in the way’s problems are solved in every aspect of our lives and from it innovative technologies will emerge. AI is the theory and development of machines that can imitate human intelligence in tasks such as visual perception speech recognition decision-making and human language translation. This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence. Research overview includes reasoning approaches problem solving knowledge representation planning learning natural language processing perception motion and manipulation social intelligence and creativity. It will allow the reader to gain a deep and broad knowledge of the latest engineering technologies of AI and Deep Learning and is an excellent resource for academic research and industry applications. | Deep Neural Network Applications

GBP 145.00
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Genetic Influences on Neural and Behavioral Functions

Genetic Influences on Neural and Behavioral Functions

Utilizing the flood of information derived from the Human Genome Project and corresponding efforts to elucidate the mouse genome Genetic Influences on Neural and Behavioral Functions provides a scholarly catalog organized logically of relations between the expression of specific genes nerve cell biology and behavior normal and abnormal in animals AND humans. Sample topics include genes in relation to schiziphrenia panic disorder epilepsy alcoholism sleep eating disorders and more.

GBP 39.99
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Artificial Intelligence Its Philosophy and Neural Context

Artificial Intelligence Its Philosophy and Neural Context

Originally published in 1986 in order to probe dispute and analyse the role of artificial intelligence in cybernetic thought and information science the author pursues this topic within its philosophical behavioral and neurophysiological contexts while drawing attention to cognitive issues. By elucidating the problems and potential associated with knowledge-based systems the book emphasized the need to examine artificial intelligence in its own right. | Artificial Intelligence Its Philosophy and Neural Context

GBP 31.99
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