Computational Intelligence Applications in Modeling and Control

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Mathematical Modeling And Computational Intelligence In Engineering Applications

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Artificial Intelligence in Control and Decision-making Systems Springer, Berlin

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This book presents an authoritative collection of contributions reporting on computational intelligence, fuzzy systems as well as artificial intelligence techniques for modelling, optimization, control and decision-making together with applications and case studies in engineering, management and economic sciences.Over the recent years, a considerable amount of research effort has been devoted to the development of advanced methods of artificial intelligence with a focus on its implementation in practice in different fields of human activity, especially for solving real control and decision-making problems in uncertainty.Dedicated to the Academician of the Polish Academy of Sciences, Professor Janusz Kacprzyk in recognition of his pioneering work, the book reports on theories, methods and new challenges in artificial intelligence, thus offering not only a timely reference guide but also a source of new ideas and inspirations for graduate students and researchers alike.The book consists of 18 chapters, presented by distinguished and experienced authors from 15 different countries (Australia, Brazil, Canada, Chile, Germany, Israel, Italy, PR China, R.N. Macedonia, Saudi Arabia, Spain, Turkey, USA, Ukraine and Vietnam). All chapters are grouped into three parts: (1) Computational Intelligence and Fuzzy Systems, (2) Artificial Intelligence Techniques in Modelling and Optimization and (3) Computational Intelligence in Control and Decision Support Processes.The book reflects recent developments and new directions in artificial intelligence, in particular, including computation method of the interval hull to solutions of interval and fuzzy interval linear systems, fuzzy Petri networks in supervisory control of Markov processes in robotic systems, fuzzy approaches for linguistic data summaries, first-approximation analysis for choosing fuzzy or neural systems and type-1 or type-2 fuzzy sets, matrix resolving functions in game dynamic problems, evolving stacking neuro-fuzzy probabilistic networks and their combined learning in online pattern recognition tasks, structural optimization of fuzzy control and decision-making systems, neural and granular fuzzy adaptive modelling, state and action abstraction for search and reinforcement learning algorithms.Among the most successful and perspective implementations in practical areas of human activity are algorithms for diagnosing neurological disorders, human-centric question-answering system, OWA operators in pensions, evaluation of the perception of public safety through fuzzy and multi-criteria approach, a multi-criteria hierarchical approach to investments, intelligent traffic signal control using the rule-based fuzzy system and generative adversarial networks in cybersecurity.The chapters provide an easy-to-follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in them can start their introduction to the topic through these references. At the same time, all of them correspond to different aspects of work-in-progress being carried out in various research groups throughout the world and, therefore, provide information on the state-of-the-art of these topics.This book is useful for researchers and students who are interested in artificial intelligence and in advanced control and decision-making systems.

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Stochastic Modeling in Economics and Finance Springer-Verlag New York Inc.

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In Part I, the fundamentals of financial thinking and elementary mathematical methods of finance are presented. The method of presentation is simple enough to bridge the elements of financial arithmetic and complex models of financial math developed in the later parts. It covers characteristics of cash flows, yield curves, and valuation of securities.§Part II is devoted to the allocation of funds and risk management: classics (Markowitz theory of portfolio), capital asset pricing model, arbitrage pricing theory, asset & liability management, value at risk. The method explanation takes into account the computational aspects.§Part III explains modeling aspects of multistage stochastic programming on a relatively accessible level. It includes a survey of existing software, links to parametric, multiobjective and dynamic programming, and to probability and statistics. It focuses on scenario-based problems with the problems of scenario generation and output analysis discussed in detail and illustrated within a case study.Unlike other books that focus only on selected specific subjects this book provides both a broad and rich cross-section of contemporary approaches to stochastic modeling in finance and economics; it is decision making oriented. The material ranges from common tools to solutions of sophisticated system problems and applications.§In Part I, the fundamentals of financial thinking and elementary mathematical methods of finance are presented. The method of presentation is simple enough to bridge the elements of financial arithmetic and complex models of financial math developed in the later parts. It covers characteristics of cash flows, yield curves, and valuation of securities.§Part II is devoted to the allocation of funds and risk management: classics (Markowitz theory of portfolio), capital asset pricing model, arbitrage pricing theory, asset & liability management, value at risk. The method explanation takes into account the computational aspects.§Part III explains modeling aspects of multistage stochastic programming on a relatively accessible level. It includes a survey of existing software, links to parametric, multiobjective and dynamic programming, and to probability and statistics. It focuses on scenario-based problems with the problems of scenario generation and output analysis discussed in detail and illustrated within a case study. Selected examples of successful applications in finance, production planning and management of technological processes and electricity generation are presented. Throughout, the emphasis is on the appropriate use of the techniques, rather than on the underlying mathematical proofs and theories.§In Part IV, the sections devoted to stochastic calculus cover also more advanced topics such as DDS Theorem or extremal martingale measures, which make it possible to treat more delicate models in Mathematical Finance (complete markets, optimal control, etc.)§Audience: Students and researchers in probability and statistics, econometrics, operations research and various fields of finance, economics, engineering, and insurance.

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Modeling, Simulation and Optimization Springer Verlag, Singapore

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This book includes selected peer-reviewed papers presented at the International Conference on Modeling, Simulation and Optimization, organized by National Institute of Technology, Silchar, Assam, India, during 3-5 August 2020. The book covers topics of modeling, simulation and optimization, including computational modeling and simulation, system modeling and simulation, device/VLSI modeling and simulation, control theory and applications, modeling and simulation of energy system and optimization. The book disseminates various models of diverse systems and includes solutions of emerging challenges of diverse scientific fields.

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Artificial Intelligence, Big Data and Data Science in Statistics Springer, Berlin

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This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book's expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and data scientists, as well as engineers and computer scientists working in related fields or applications.

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Artificial Intelligence for Edge Computing Springer, Berlin

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It is undeniable that the recent revival of artificial intelligence (AI) has significantly changed the landscape of science in many application domains, ranging from health to defense and from conversational interfaces to autonomous cars. With terms such as "Google Home", "Alexa", and "ChatGPT" becoming household names, the pervasive societal impact of AI is clear. Advances in AI promise a revolution in our interaction with the physical world, a domain where computational intelligence has always been envisioned as a transformative force toward a better tomorrow. Depending on the application family, this domain is often referred to as Ubiquitous Computing, Cyber-Physical Computing, or the Internet of Things. The underlying vision is driven by the proliferation of cheap embedded computing hardware that can be integrated easily into myriads of everyday devices from consumer electronics, such as personal wearables and smart household appliances, to city infrastructure and industrial process control systems. One common trait across these applications is that the data that the application operates on come directly (typically via sensors) from the physical world. Thus, from the perspective of communication network infrastructure, the data originate at the network edge. From a performance standpoint, there is an argument to be made that such data should be processed at the point of collection. Hence, a need arises for Edge AI -- a genre of AI where the inference, and sometimes even the training, are performed at the point of need, meaning at the edge where the data originate.The book is broken down into three parts: core problems, distributed problems, and other cross-cutting issues. It explores the challenges arising in Edge AI contexts. Some of these challenges (such as neural network model reduction to fit resource-constrained hardware) are unique to the edge environment. They need a novel category of solutions that do not parallel more typical concerns in mainstream AI. Others are adaptations of mainstream AI challenges to the edge space. An example is overcoming the cost of data labeling. The labeling problem is pervasive, but its solution in the IoT application context is different from other contexts. This book is not a survey of the state of the art. With thousands of publications appearing in AI every year, such a survey is doomed to be incomplete on arrival. It is also not a comprehensive coverage of all the problems in the space of Edge AI. Different applications pose different challenges, and a more comprehensive coverage should be more application specific. Instead, this book covers some of the more endemic challenges across the range of IoT/CPS applications. To offer coverage in some depth, we opt to cover mainly one or a few representative solutions for each of these endemic challenges in sufficient detail, rather that broadly touching on all relevant prior work. The underlying philosophy is one of illustrating by example. The solutions are curated to offer insight into a way of thinking that characterizes Edge AI research and distinguishes its solutions from their more mainstream counterparts.

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Robotic Welding, Intelligence and Automation Springer International Publishing AG

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The primary aim of this volume is to provide researchers and engineers from both academic and industry with up-to-date coverage of new results in the field of robotic welding, intelligent systems and automation. The book is mainly based on papers selected from the 2014 International Conference on Robotic Welding, Intelligence and Automation (RWIA'2014), held Oct. 25-27, 2014, at Shanghai, China. The articles show that the intelligentized welding manufacturing (IWM) is becoming an inevitable trend with the intelligentized robotic welding as the key technology. The volume is divided into four logical parts: Intelligent Techniques for Robotic Welding, Sensing of Arc Welding Processing, Modeling and Intelligent Control of Welding Processing, as well as Intelligent Control and its Applications in Engineering.§§

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Soft Sensors for Monitoring and Control of Industrial Processes Springer London Ltd

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Soft sensors are inferential estimators, drawing conclusions from process observations when hardware sensors are unavailable or unsuitable; they have an important auxiliary role in sensor validation when performance declines through senescence or fault accumulation.§The non-linear behaviour exhibited by many industrial processes can be usefully modelled with the techniques of computational intelligence: neural networks; fuzzy systems and nonlinear partial least squares.§Soft Sensors for Monitoring and Control of Industrial Processes underlines the real usefulness of each approach and the sensitivity of the individual steps in soft-sensor design to the choice of one or the other. Design paths are suggested and readers shown how to evaluate the effects of their choices. All the case studies reported, resulting from collaborations between the authors and a number of industrial partners, raised challenging soft-sensor-design problems. The applications of soft sensors presented in this volume are designed to cope with the whole range from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation. Some of the soft sensors developed here are implemented on-line at industrial plants.§Features:§soft-sensor design;§advice on data selection and choice of model structure;§model validation;§strategies for the improvement of soft-sensor performance;§uses of soft sensors in fault detection and sensor validation;§soft sensors in use in industrial applications such as a debutanizer column and a sulfur recovery unit.§This monograph guides interested readers researchers, graduate students and industrial process technologists through the design of their own soft sensors. It is self-contained with full references and appraisal of existing literature and data sets for some of the case studies can be downloaded from springer.com.This book reviews current design paths for soft sensors, and guides readers in evaluating different choices. The book presents case studies resulting from collaborations between the authors and industrial partners. The solutions presented, some of which are implemented on-line in industrial plants, are designed to cope with a wide range of applications from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation.Soft sensors are inferential estimators, drawing conclusions from process observations when hardware sensors are unavailable or unsuitable; they have an important auxiliary role in sensor validation when performance declines through senescence or fault accumulation.§The non-linear behaviour exhibited by many industrial processes can be usefully modelled with the techniques of computational intelligence: neural networks; fuzzy systems and nonlinear partial least squares.§Soft Sensors for Monitoring and Control of Industrial Processes underlines the real usefulness of each approach and the sensitivity of the individual steps in soft-sensor design to the choice of one or the other. Design paths are suggested and readers shown how to evaluate the effects of their choices. All the case studies reported, resulting from collaborations between the authors and a number of industrial partners, raised challenging soft-sensor-design problems. The applications of soft sensors presented in this volume are designed to cope with the whole range from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation. Some of the soft sensors developed here are implemented on-line at industrial plants.§Features: soft-sensor design; advice on data selection and choice of model structure; model validation; strategies for the improvement of soft-sensor performance; uses of soft sensors in fault detection and sensor validation; soft sensors in use in industrial applications such as a debutanizer column and a sulfur recovery unit.§This monograph guides interested readers researchers, graduate students and industrial process technologists through the design of their own soft sensors. It is self-contained with full references and appraisal of existing literature and data sets for some of the case studies can be downloaded from springer.com. §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.

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Intelligent Computing Theories and Methodologies Springer International Publishing AG

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This two-volume set LNCS 9225 and LNCS 9226 constitutes - in conjunction with the volume LNAI 9227 - the refereed proceedings of the 11th International Conference on Intelligent Computing, ICIC 2015, held in Fuzhou, China, in August 2015. The 191 full papers and 42 short papers of this volume were carefully reviewed and selected from 191 submissions. The papers are organized in topical sections such as evolutionary computation and learning; compressed sensing, sparse coding and social computing; neural networks, nature inspired computing and optimization; pattern recognition and signal processing; image processing; biomedical informatics theory and methods; differential evolution, particle swarm optimization and niche technology; intelligent computing and knowledge discovery and data mining; soft computing and machine learning; computational biology, protein structure and function prediction; genetic algorithms; artificial bee colony algorithms; swarm intelligence and optimization; social computing; information security; virtual reality and human-computer interaction; healthcare informatics theory and methods; unsupervised learning; collective intelligence; intelligent computing in robotics; intelligent computing in communication networks; intelligent control and automation; intelligent data analysis and prediction; gene expression array analysis; gene regulation modeling and analysis; protein-protein interaction prediction; biology inspired computing and optimization; analysis and visualization of large biological data sets; motif detection; biomarker discovery; modeling; simulation; and optimization of biological systems; biomedical data modeling and mining; intelligent computing in biomedical signal/image analysis; intelligent computing in brain imaging; neuroinformatics; cheminformatics; intelligent computing in computational biology; computational genomics; special session on biomedical data integration and mining in the era of big data; special session on big data analytics; special session on artificial intelligence for ambient assisted living; and special session on swarm intelligence with discrete dynamics.

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Military Logistics Springer International Publishing AG

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This book highlights recent advances in the development of effective modeling and solution approaches to enhance the performance of military logistics. It seeks to further research in global defense-related topics, including military operations, governmental operations and security, as well as nation support. Additionally its purpose is to promote the global exchange of information and ideas amongst developers and users of military operations research tools and techniques.§§Over the course of its nine chapters, this edited volume addresses significant issues in military logistics including: a) Restructuring processes via OR methods aimed at improving the efficiency and effectiveness of the military logistics, b) Sense-and-Respond logistics prediction and coordination techniques that provide competitive advantage, spanning the full range of military operations across the strategic, operational and tactical levels of war, c) Procurement and auctioning, d) Inventory and stock control theories and applications, e) Military transport and logistical equipment, and, f) Maintenance, repair and overhaul on operational capability in general and equipment availability.§§The book aims to bridge the gap between the abundant literature on commercial logistics and its scarce defense & combat counterpart. This collection of useful insights into new trends and research will offer an ideal reference for practitioners and army related personnel interested in integrating scientific rigor to improve logistics management within defense organizations & agencies. Ultimately this book should provide a relevant platform for the latest contributions of operations management, operations research, and computational intelligence towards the enhancement of military logistics.§

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Military Logistics Springer International Publishing AG

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This book highlights recent advances in the development of effective modeling and solution approaches to enhance the performance of military logistics. It seeks to further research in global defense-related topics, including military operations, governmental operations and security, as well as nation support. Additionally its purpose is to promote the global exchange of information and ideas amongst developers and users of military operations research tools and techniques.Over the course of its nine chapters, this edited volume addresses significant issues in military logistics including: a) Restructuring processes via OR methods aimed at improving the efficiency and effectiveness of the military logistics, b) Sense-and-Respond logistics prediction and coordination techniques that provide competitive advantage, spanning the full range of military operations across the strategic, operational and tactical levels of war, c) Procurement and auctioning, d) Inventory and stock control theories and applications, e) Military transport and logistical equipment, and, f) Maintenance, repair and overhaul on operational capability in general and equipment availability.The book aims to bridge the gap between the abundant literature on commercial logistics and its scarce defense & combat counterpart. This collection of useful insights into new trends and research will offer an ideal reference for practitioners and army related personnel interested in integrating scientific rigor to improve logistics management within defense organizations & agencies. Ultimately this book should provide a relevant platform for the latest contributions of operations management, operations research, and computational intelligence towards the enhancement of military logistics.

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New Trends in Nanotechnology and Fractional Calculus Springer Verlag

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In recent years fractional calculus has played an important role in various fields such as mechanics, electricity, chemistry, biology, economics, modeling, identification, control theory and signal processing. The scope of this book is to present the state of the art in the study of fractional systems and the application of fractional differentiation. Furthermore, the manufacture of nanowires is important for the design of nanosensors and the development of high-yield thin films is vital in procuring clean solar energy. This wide range of applications is of interest to engineers, physicists and mathematicians.Table of contentsPreface;New trends in nanotechnology: Novel Molecular Diodes Developed by Chemial Conjugation of Carbon Nanotubes with Peptide Nucleic Acid, by Krishna V. Singh, Miroslav Penchev, Xiaoye Jing, Alfredo A. Martinez-Morales, Cengiz S. Ozkan and Mihri Ozkan;Hybrid Single Walled Carbon Nanotube FETs for High Fidelity DNA Detection, by Xu Wang, Mihri Ozkan, Gurer Budak, Ziya B. Guvenc and Cengiz S. Ozkan;Towards integrated nanoelectronic and photonic devices, by Alexander Quandt, Maurizio Ferrari and Giancarlo C. Righini;New noninvasive methods for

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Machine Learning in Finance Springer Nature Switzerland AG

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This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

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Machine Learning in Finance Springer Nature Switzerland AG

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This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend increasing toward computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Sklep: Libristo.pl