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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
  • Author : Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi
  • Publisher :Unknown
  • Release Date :2020-07-03
  • Total pages :328
  • ISBN : 9780128193662
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Summary : Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
  • Author : Chris Aldrich,Lidia Auret
  • Publisher :Unknown
  • Release Date :2013-06-15
  • Total pages :374
  • ISBN : 9781447151852
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Summary : This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.

Multivariate Statistical Process Control

Multivariate Statistical Process Control
  • Author : Zhiqiang Ge,Zhihuan Song
  • Publisher :Unknown
  • Release Date :2012-11-28
  • Total pages :194
  • ISBN : 9781447145134
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Summary : Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas. Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Advanced methods for fault diagnosis and fault-tolerant control

Advanced methods for fault diagnosis and fault-tolerant control
  • Author : Steven X. Ding
  • Publisher :Unknown
  • Release Date :2021
  • Total pages :229
  • ISBN : 9783662620045
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Summary :

Advances in Production Management Systems. Production Management for Data-Driven, Intelligent, Collaborative, and Sustainable Manufacturing

Advances in Production Management Systems. Production Management for Data-Driven, Intelligent, Collaborative, and Sustainable Manufacturing
  • Author : Ilkyeong Moon,Gyu M. Lee,Jinwoo Park,Dimitris Kiritsis,Gregor von Cieminski
  • Publisher :Unknown
  • Release Date :2018-08-24
  • Total pages :570
  • ISBN : 9783319997049
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Summary : The two-volume set IFIP AICT 535 and 536 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2018, held in Seoul, South Korea, in August 2018. The 129 revised full papers presented were carefully reviewed and selected from 149 submissions. They are organized in the following topical sections: lean and green manufacturing; operations management in engineer-to-order manufacturing; product-service systems, customer-driven innovation and value co-creation; collaborative networks; smart production for mass customization; global supply chain management; knowledge based production planning and control; knowledge based engineering; intelligent diagnostics and maintenance solutions for smart manufacturing; service engineering based on smart manufacturing capabilities; smart city interoperability and cross-platform implementation; manufacturing performance management in smart factories; industry 4.0 - digital twin; industry 4.0 - smart factory; and industry 4.0 - collaborative cyber-physical production and human systems.

Machine Learning for Cyber Physical Systems

Machine Learning for Cyber Physical Systems
  • Author : Jürgen Beyerer,Alexander Maier,Oliver Niggemann
  • Publisher :Unknown
  • Release Date :2019-04-09
  • Total pages :87
  • ISBN : 9783662590843
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Summary : The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 25th-26th, 2017. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

A Dictionary of Chemical Engineering

A Dictionary of Chemical Engineering
  • Author : Carl Schaschke
  • Publisher :Unknown
  • Release Date :2014-01-09
  • Total pages :448
  • ISBN : 9780191002694
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Summary : A Dictionary of Chemical Engineering is one of the latest additions to the market leading Oxford Paperback Reference series. In over 3,400 concise and authoritative A to Z entries, it provides definitions and explanations for chemical engineering terms in areas including: materials, energy balances, reactions, separations, sustainability, safety, and ethics. Naturally, the dictionary also covers many pertinent terms from the fields of chemistry, physics, biology, and mathematics. Useful entry-level web links are listed and regularly updated on a dedicated companion website to expand the coverage of the dictionary. Comprehensively cross-referenced and complemented by over 60 line drawings, this excellent new volume is the most authoritative dictionary of its kind. It is an essential reference source for students of chemical engineering, for professionals in this field (as well as related disciplines such as applied chemistry, chemical technology, and process engineering), and for anyone with an interest in the subject.

Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems
  • Author : L.H. Chiang,E.L. Russell,R.D. Braatz
  • Publisher :Unknown
  • Release Date :2012-12-06
  • Total pages :279
  • ISBN : 9781447103479
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Summary : Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.

Thermodynamic and Transport Properties of Fluids. SI Units

Thermodynamic and Transport Properties of Fluids. SI Units
  • Author : Y. R. Mayhew
  • Publisher :Unknown
  • Release Date :1977
  • Total pages :20
  • ISBN : OCLC:17961006
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Summary :

Service Orientation in Holonic and Multi-Agent Manufacturing

Service Orientation in Holonic and Multi-Agent Manufacturing
  • Author : Theodor Borangiu,Damien Trentesaux,André Thomas,Sergio Cavalieri
  • Publisher :Unknown
  • Release Date :2018-12-12
  • Total pages :462
  • ISBN : 9783030030032
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Summary : This book gathers the peer-reviewed papers presented at the 8th edition of the International Workshop “Service Orientation in Holonic and Multi-Agent Manufacturing – SOHOMA’18” held at the University of Bergamo, Italy on June 11–12, 2018. The objective of the SOHOMA annual workshops is to foster innovation in smart and sustainable manufacturing and logistics systems by promoting new concepts, methods and solutions that use service orientation of agent-based control technologies with distributed intelligence. Reflecting the theme of SOHOMA’18: “Digital transformation of manufacturing with agent-based control and service orientation of Internet-scale platforms”, the research included focuses on how the digital transformation, as advocated by the “Industry 4.0”, “Industrial Internet of Things”, “Cyber-Physical Production Systems” and “Cloud Manufacturing” frameworks, improves the efficiency, agility and sustainability of manufacturing processes, products, and services, and how it relates to the interaction between the physical and informational worlds, which is implemented in the virtualization of products, processes and resources managed as services.

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis
  • Author : Majdi Mansouri,Mohamed-Faouzi Harkat,Hazem N. Nounou,Mohamed N. Nounou
  • Publisher :Unknown
  • Release Date :2020-02-05
  • Total pages :322
  • ISBN : 9780128191651
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Summary : Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely. Outlines latent variable based hypothesis testing fault detection techniques to enhance monitoring processes represented by linear or nonlinear input-space models (such as PCA) or input-output models (such as PLS) Explains multiscale latent variable based hypothesis testing fault detection techniques using multiscale representation to help deal with uncertainty in the data and minimize its effect on fault detection Includes interval PCA (IPCA) and interval PLS (IPLS) fault detection methods to enhance the quality of fault detection Provides model-based detection techniques for the improvement of monitoring processes using state estimation-based fault detection approaches Demonstrates the effectiveness of the proposed strategies by conducting simulation and experimental studies on synthetic data

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models
  • Author : Keith R. Holdaway,Duncan H. B. Irving
  • Publisher :Unknown
  • Release Date :2017-10-04
  • Total pages :368
  • ISBN : 9781119302582
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Summary : Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. Apply data-driven modeling concepts in a geophysical and petrophysical context Learn how to get more information out of models and simulations Add value to everyday tasks with the appropriate Big Data application Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.

Big Data Application in Power Systems

Big Data Application in Power Systems
  • Author : Reza Arghandeh,Yuxun Zhou
  • Publisher :Unknown
  • Release Date :2017-11-27
  • Total pages :480
  • ISBN : 9780128119693
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Summary : Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids. Provides expert analysis of the latest developments by global authorities Contains detailed references for further reading and extended research Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data

Structural Health Monitoring

Structural Health Monitoring
  • Author : Charles R. Farrar,Keith Worden
  • Publisher :Unknown
  • Release Date :2012-11-19
  • Total pages :656
  • ISBN : 9781118443217
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Summary : Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions. Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies. Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.

Data-Driven Prediction for Industrial Processes and Their Applications

Data-Driven Prediction for Industrial Processes and Their Applications
  • Author : Jun Zhao,Wei Wang,Chunyang Sheng
  • Publisher :Unknown
  • Release Date :2018-08-20
  • Total pages :443
  • ISBN : 9783319940519
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Summary : This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

Hydrological Data Driven Modelling

Hydrological Data Driven Modelling
  • Author : Renji Remesan,Jimson Mathew
  • Publisher :Unknown
  • Release Date :2014-11-03
  • Total pages :250
  • ISBN : 9783319092355
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Summary : This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

Modeling and Control of Batch Processes

Modeling and Control of Batch Processes
  • Author : Prashant Mhaskar,Abhinav Garg,Brandon Corbett
  • Publisher :Unknown
  • Release Date :2018-11-28
  • Total pages :335
  • ISBN : 9783030041403
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Summary : Modeling and Control of Batch Processes presents state-of-the-art techniques ranging from mechanistic to data-driven models. These methods are specifically tailored to handle issues pertinent to batch processes, such as nonlinear dynamics and lack of online quality measurements. In particular, the book proposes: a novel batch control design with well characterized feasibility properties; a modeling approach that unites multi-model and partial least squares techniques; a generalization of the subspace identification approach for batch processes; and applications to several detailed case studies, ranging from a complex simulation test bed to industrial data. The book’s proposed methodology employs statistical tools, such as partial least squares and subspace identification, and couples them with notions from state-space-based models to provide solutions to the quality control problem for batch processes. Practical implementation issues are discussed to help readers understand the application of the methods in greater depth. The book includes numerous comments and remarks providing insight and fundamental understanding into the modeling and control of batch processes. Modeling and Control of Batch Processes includes many detailed examples of industrial relevance that can be tailored by process control engineers or researchers to a specific application. The book is also of interest to graduate students studying control systems, as it contains new research topics and references to significant recent work. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Statistical Methods for Industrial Process Control

Statistical Methods for Industrial Process Control
  • Author : David .C. Drain
  • Publisher :Unknown
  • Release Date :1997-02-01
  • Total pages :476
  • ISBN : 0412085119
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Summary : To practice engineering effectively, engineers must need to have a working knowledge of statistical concepts and methods. What they do not need is a background heavy on statistical theory and number crunching. Statistical Methods for Industrial Process Control provides the practical statistics foundation engineers can immediately apply to the work they do every day, regardless of their industry or specialty. The author illustrates statistical concepts and methods with authentic semiconductor manufacturing process examples-integrated circuit fabrication is an exceedingly rich medium for communicating statistical concepts. However, once learned, these concepts and methods can easily be extended and applied to a variety of other industries. The text emphasizes the application of statistical tools, rather than statistical theory. Modern advances in statistical software have made tedious computations and formula memorization unnecessary. Therefore, the author demonstrates software use throughout the book and supplies MINITAB examples and SAS programs. Review problems at the end of each chapter challenge and deepen readers' understanding of the material. Statistical Methods for Industrial Process Control addresses topics that support the work engineers do, rather than educate them as statisticians, and these topics also reflect modern usage. It effectively introduces novice engineers to a fascinating industry and enables experienced engineers to build upon their existing knowledge and learn new skills.

Financial Signal Processing and Machine Learning

Financial Signal Processing and Machine Learning
  • Author : Ali N. Akansu,Sanjeev R. Kulkarni,Dmitry M. Malioutov
  • Publisher :Unknown
  • Release Date :2016-05-31
  • Total pages :320
  • ISBN : 9781118745670
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Summary : The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Data-Driven Modeling for Additive Manufacturing of Metals

Data-Driven Modeling for Additive Manufacturing of Metals
  • Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,National Materials and Manufacturing Board,Board on Mathematical Sciences and Analytics
  • Publisher :Unknown
  • Release Date :2019-10-09
  • Total pages :78
  • ISBN : 9780309494236
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Summary : Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests. The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.

Handbook of Neural Computation

Handbook of Neural Computation
  • Author : Pijush Samui,Sanjiban Sekhar Roy,Valentina E. Balas
  • Publisher :Unknown
  • Release Date :2017-07-18
  • Total pages :658
  • ISBN : 9780128113196
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Summary : Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods