Articles by "Algorithms"

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Showing posts with label Algorithms. Show all posts

October 25, 2019 , ,
Michael Z. Zgurovsky, Yuriy P. Zaychenko ... 304 pages - Publisher: Springer (May, 2020) ... Language: English - ISBN-10: 3030143007 - ISBN-13: 978-3030143008.

The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe–Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.

October 23, 2019 , , ,
Olaf Wolkenhauer  ... 296 pages - Publisher: Wiley-Interscience; (July, 2001) ... Language: English - ISBN-10: 0471416568 - ISBN-13: 978-0471416562.

A survey of the philosophical implications and practical applications of fuzzy systems: Fuzzy mathematical concepts such as fuzzy sets, fuzzy logic, and similarity relations represent one of the most exciting currents in modern engineering and have great potential in applications ranging from control theory to bioinformatics. Data Engineering guides the reader through a number of concepts interconnected by fuzzy mathematics and discusses these concepts from a systems engineering perspective to showcase the continuing vitality, attractiveness, and applicability of fuzzy mathematics.

The author discusses the fundamental aspects of data analysis, systems modeling, and uncertainty calculi. He avoids a narrow discussion of specialized methodologies and takes a holistic view of the nature and application of fuzzy systems, considering principles, paradigms, and methodologies along the way. This broad coverage includes: * Fundamentals of modeling, identification, and clustering * System analysis * Uncertainty techniques * Random-set modeling and identification * Fuzzy inference engines * Fuzzy classification, control, and mathematics. In the important emerging field of bioinformatics, the book sets out how to encode a natural system in mathematical models, describes methods to identify interrelationships and interactions from data, and thereby helps the practitioner to decide which variables to measure and why. Data Engineering serves as an up-to-date and informative survey of the theoretical and practical tools for analyzing complex systems. It offers a unique treatment of complex issues that is accessible to students and researchers from a variety of backgrounds.

Ross Baldick ... 792 pages - Publisher: Cambridge University Press; (January, 2009) ... Language: English - ISBN-10: 0521100283 - ISBN-13: 978-0521100281.

The starting point in the formulation of any numerical problem is to take an intuitive idea about the problem in question and to translate it into precise mathematical language. This book provides step-by-step descriptions of how to formulate numerical problems so that they can be solved by existing software. It examines various types of numerical problems and develops techniques for solving them. A number of engineering case studies are used to illustrate in detail the formulation process. The case studies motivate the development of efficient algorithms that involve, in some cases, transformation of the problem from its initial formulation into a more tractable form.

October 13, 2019 , , ,
Mathworks Matlab R2019b v9.7.0 [Size: 21.5 GB] ... MATLAB is a highlevel language and interactive environment that is used by millions of engineers and scientists around the world. It allows you to explore and visualize ideas and collaborate in various disciplines, including signal and image processing, communications, management systems and financial engineering. Whether you’re analyzing data, developing algorithms, or creating models, MATLAB is designed for the way you think and the work you do. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques.

Features of Mathworks Matlab R2019: Organize live scripts using additional subheading styles + Navigate within a live script using internal hyperlinks + Create new and open existing live scripts faster + Use string arrays in MATLAB, Simulink, and Stateflow + Improved performance of set operations with enumerations + Create a polyshape object from a 2-D triangulation + Generate random numbers using Threefry and Philox algorithms + Customize node and edge labels with font properties + Explore data with panning, zooming, data tips, and 3-D rotation enabled by default + Visualize grouped data as a scatter plot with marginal histograms + Generate improved code when importing from spreadsheets + Write tall arrays in a variety of formats to local or remote locations + Transform table or timetable data by groups + Add and configure date selection components on the App Designer canvas + Pass multidimensional numeric or logical arrays between MATLAB and Python.

October 08, 2019 , , ,
Rudra Pratap ... 288 pages - Publisher: Oxford Univ. Press; (November, 2009) ... Language: English - ISBN-10: 0199731241 - ISBN-13: 978-0199731244.

MATLAB, a software package for high-performance numerical computation and visualization, is one of the most widely used tools in the engineering field today. Its broad appeal lies in its interactive environment, which features hundreds of built-in functions for technical computation, graphics, and animation. In addition, MATLAB provides easy extensibility with its own high-level programming language. Enhanced by fun and appealing illustrations, Getting Started with MATLAB employs a casual, accessible writing style that shows users how to enjoy using MATLAB.

Features: * Discusses new features and applications, including the new engine of symbolic computation in MATLAB 7.8 (released March 2009) * Provides two sets of self guided tutorials for learning essential features of MATLAB * Includes updated commands, examples, figure, and graphs * Familiarizes users with MATLAB in just a few hours though self-guided lessons * Covers elementary, advanced, and special functions * Supplements any course that uses MATLAB * Works as a stand-alone tutorial and reference.

October 07, 2019 , ,
Ronald E. Miller ... 676 pages - Publisher: Wiley-Interscience; (November, 1999) ... Language: English - ISBN-10: 0471351695 - ISBN-13: 978-0471351696.

A thorough and highly accessible resource for analysts in a broad range of social sciences: Optimization: Foundations and Applications presents a series of approaches to the challenges faced by analysts who must find the best way to accomplish particular objectives, usually with the added complication of constraints on the available choices. Award-winning educator Ronald E. Miller provides detailed coverage of both classical, calculus-based approaches and newer, computer-based iterative methods. Dr. Miller lays a solid foundation for both linear and nonlinear models and quickly moves on to discuss applications, including iterative methods for root-finding and for unconstrained maximization, approaches to the inequality constrained linear programming problem, and the complexities of inequality constrained maximization and minimization in nonlinear problems. Other important features include: More than 200 geometric interpretations of algebraic results, emphasizing the intuitive appeal of mathematics + Classic results mixed with modern numerical methods to aid users of computer programs + Extensive appendices containing mathematical details important for a thorough understanding of the topic. With special emphasis on questions most frequently asked by those encountering this material for the first time, Optimization: Foundations and Applications is an extremely useful resource for professionals in such areas as mathematics, engineering, economics and business, regional science, geography, sociology, political science, management and decision sciences, public policy analysis, and numerous other social sciences.

October 07, 2019 , , ,
Steven J. Miller ... 327 pages - Publisher: American Mathematical Society; (December, 2017) ... Language: English - ISBN-10: 1470441144 - ISBN-13: 978-1470441142.

Optimization Theory is an active area of research with numerous applications; many of the books are designed for engineering classes, and thus have an emphasis on problems from such fields. Covering much of the same material, there is less emphasis on coding and detailed applications as the intended audience is more mathematical. There are still several important problems discussed (especially scheduling problems), but there is more emphasis on theory and less on the nuts and bolts of coding. A constant theme of the text is the ``why'' and the ``how'' in the subject. Why are we able to do a calculation efficiently? How should we look at a problem? Extensive effort is made to motivate the mathematics and isolate how one can apply ideas/perspectives to a variety of problems. As many of the key algorithms in the subject require too much time or detail to analyze in a first course (such as the run-time of the Simplex Algorithm), there are numerous comparisons to simpler algorithms which students have either seen or can quickly learn (such as the Euclidean algorithm) to motivate the type of results on run-time savings.

September 27, 2019 , ,
John Wolberg ... 250 pages - Publisher: Springer Berlin Heidelberg; (February, 2006) ... Language: English - ASIN: B000VHULZG by Amazon.

The preferred method of data analysis of quantitative experiments is the method of least squares. Often, however, the full power of the method is overlooked and very few books deal with this subject at the level that it deserves. The purpose of Data Analysis Using the Method of Least Squares is to fill this gap and include the type of information required to help scientists and engineers apply the method to problems in their special fields of interest. In addition, graduate students in science and engineering doing work of experimental nature can benefit from this book. Particularly, both linear and non-linear least squares, the use of experimental error estimates for data weighting, procedures to include prior estimates, methodology for selecting and testing models, prediction analysis, and some non-parametric methods are discussed.

Andreas Müller, Sarah Guido ... 400 pages - Publisher: O'Reilly Media; (October, 2016) ... Language: English - ISBN-10: 1449369413 - ISBN-13: 978-1449369415.

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn: Fundamental concepts and applications of machine learning + Advantages and shortcomings of widely used machine learning algorithms + How to represent data processed by machine learning, including which data aspects to focus on + Advanced methods for model evaluation and parameter tuning + The concept of pipelines for chaining models and encapsulating your workflow + Methods for working with text data, including text-specific processing techniques + Suggestions for improving your machine learning and data science skills.

September 16, 2019 , ,
Wendy L. Martinez, Angel Martinez, Jeffrey Solka ... 536 pages - Publisher: CRC Press; 2nd edition (December, 2010) ... Language: English - ISBN-10: 1439812209 - ISBN-13: 978-1439812204

Since the publication of the bestselling first edition, many advances have been made in exploratory data analysis (EDA). Covering innovative approaches for dimensionality reduction, clustering, and visualization, Exploratory Data Analysis with MATLAB, Second Edition uses numerous examples and applications to show how the methods are used in practice. New to the Second Edition: Discussions of nonnegative matrix factorization, linear discriminant analysis, curvilinear component analysis, independent component analysis, and smoothing splines - An expanded set of methods for estimating the intrinsic dimensionality of a data set - Several clustering methods, including probabilistic latent semantic analysis and spectral-based clustering - Additional visualization methods, such as a rangefinder boxplot, scatterplots with marginal histograms, biplots, and a new method called Andrews’ images -Instructions on a free MATLAB GUI toolbox for EDA... Like its predecessor, this edition continues to focus on using EDA methods, rather than theoretical aspects. The MATLAB codes for the examples, EDA toolboxes, data sets, and color versions of all figures are available for download at http://pi-sigma.info.

September 10, 2019 , , ,
Gowrishankar S., Veena A. ... 464 pages - Publisher: Chapman and Hall/CRC; (November, 2018) ... Language: English - ISBN-10: 0815394373 - ISBN-13: 978-0815394372

Introduction to Python Programming is written for students who are beginners in the field of computer programming. This book presents an intuitive approach to the concepts of Python Programming for students. This book differs from traditional texts not only in its philosophy but also in its overall focus, level of activities, development of topics, and attention to programming details. The contents of the book are chosen with utmost care after analyzing the syllabus for Python course prescribed by various top universities in USA, Europe, and Asia. Since the prerequisite know-how varies significantly from student to student, the book’s overall overture addresses the challenges of teaching and learning of students which is fine-tuned by the authors’ experience with large sections of students. This book uses natural language expressions instead of the traditional shortened words of the programming world. This book has been written with the goal to provide students with a textbook that can be easily understood and to make a connection between what students are learning and how they may apply that knowledge.

Clarisse Dhaenens, Laetitia Jourdan ... 213 pages - Publisher: Wiley-ISTE; (August, 2016) ... Language: English - ASIN: B01KZO6P4U by Amazon

Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.

Patrick Laurie Davies ... 320 pages - Publisher: Chapman and Hall/CRC; (July, 2014) ... Language: English - ASIN: B00L2LDVM0 by Amazon

The First Detailed Account of Statistical Analysis That Treats Models as Approximations: The idea of truth plays a role in both Bayesian and frequentist statistics. The Bayesian concept of coherence is based on the fact that two different models or parameter values cannot both be true. Frequentist statistics is formulated as the problem of estimating the "true but unknown" parameter value that generated the data. Forgoing any concept of truth, Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis presents statistical analysis/inference based on approximate models. Developed by the author, this approach consistently treats models as approximations to data, not to some underlying truth.

The author develops a concept of approximation for probability models with applications to: Discrete data + Location scale + Analysis of variance (ANOVA) + Nonparametric regression, image analysis, and densities + Time series + Model choice. The book first highlights problems with concepts such as likelihood and efficiency and covers the definition of approximation and its consequences. A chapter on discrete data then presents the total variation metric as well as the Kullback–Leibler and chi-squared discrepancies as measures of fit. After focusing on outliers, the book discusses the location-scale problem, including approximation intervals, and gives a new treatment of higher-way ANOVA. The next several chapters describe novel procedures of nonparametric regression based on approximation. The final chapter assesses a range of statistical topics, from the likelihood principle to asymptotics and model choice.

Brian Hahn, Daniel T. Valentine ... 428 pages - Publisher: Academic Press; 7th edition (April, 2019) ... Language: English - ISBN-10: 0081029977 - ISBN-13: 978-0081029978

Essential MATLAB for Engineers and Scientists, Seventh Edition, provides a concise, balanced overview of MATLAB's functionality, covering both fundamentals and applications. The essentials are illustrated throughout, featuring complete coverage of the software's windows and menus. Program design and algorithm development are presented, along with many examples from a wide range of familiar scientific and engineering areas. This edition has been updated to include the latest MATLAB versions through 2018b. This is an ideal book for a first course on MATLAB, but is also ideal for an engineering problem-solving course using MATLAB. Updated to include all the newer features through MATLAB R2018b + Includes new chapter on useful toolboxes + Provides additional examples on engineering applications.

Ying Tan ... 323 pages - Publisher: Springer; (October, 2015) ... Language: English - ISBN-10: 3662463520 - ISBN-13: 978-3662463529

This book is devoted to the state-of-the-art in all aspects of fireworks algorithm (FWA), with particular emphasis on the efficient improved versions of FWA. It describes the most substantial theoretical analysis including basic principle and implementation of FWA and modeling and theoretical analysis of FWA. It covers exhaustively the key recent significant research into the improvements of FWA so far. In addition, the book describes a few advanced topics in the research of FWA, including multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, several successful applications of FWA on non-negative matrix factorization (NMF), text clustering, pattern recognition, and seismic inversion problem, and swarm robotics, are illustrated in details, which might shed new light on more real-world applications in future. Addressing a multidisciplinary topic, it will appeal to researchers and professionals in the areas of metahuristics, swarm intelligence, evolutionary computation, complex optimization solving, etc.

Marco Dorigo, Thomas Stützle ... 319 pages - Publisher: Bradford Book; (June, 2004) ... Language: English - ISBN-10: 0262042193 - ISBN-13: 978-0262042192

The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

R. Venkata Rao ... 334 pages - Publisher: Springer; (June, 2018) ... Language: English - ISBN-10: 331978921X - ISBN-13: 978-3319789217

This book introduces readers to the “Jaya” algorithm, an advanced optimization technique that can be applied to many physical and engineering systems. It describes the algorithm, discusses its differences with other advanced optimization techniques, and examines the applications of versions of the algorithm in mechanical, thermal, manufacturing, electrical, computer, civil and structural engineering. In real complex optimization problems, the number of parameters to be optimized can be very large and their influence on the goal function can be very complicated and nonlinear in character. Such problems cannot be solved using classical methods and advanced optimization methods need to be applied. The Jaya algorithm is an algorithm-specific parameter-less algorithm that builds on other advanced optimization techniques. The application of Jaya in several engineering disciplines is critically assessed and its success compared with other complex optimization techniques such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and other recently developed algorithms.

Ke-Lin Du, M. N. S. Swamy ... 489 pages - Publisher: Springer; (December, 2016) ... Language: English - ISBN-10: 3319454013 - ISBN-13: 978-3319454016

This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing.  Over 100 different types of these methods are discussed in detail.  The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material.  Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others.  General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described.  Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics.  Introduced in the appendix are some benchmarks for the evaluation of metaheuristics. Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science.  It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.

Artificial Intelligence Masterclass: Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI Models [Size: 6.10 GB] ... Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Sounds tempting right… Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch. In this course, we will teach you how to develop the most powerful Artificial intelligence model based on the most robust Hybrid Intelligent System. So far this model proves to be the best state of the art AI ever created beating its predecessors at all the AI competitions with incredibly high scores. This Hybrid Model is aptly named the Full World Model, and it combines all the state of the art models of the different AI branches, including Deep Learning, Deep Reinforcement Learning, Policy Gradient, and even, Deep NeuroEvolution. Learn how to combine the below models in order to achieve best performing artificial intelligence system: Fully-Connected Neural Networks + Convolutional Neural Networks + Recurrent Neural Networks + Variational AutoEncoders + Mixed Density Networks + Genetic Algorithms + Evolution Strategies + Covariance Matrix Adaptation Evolution Strategy (CMA-ES) + Parameter-Exploring Policy Gradients.

April 01, 2019 , , ,
Jasbir S. Arora ... 896 pages - Publisher: Academic Press; 3rd edition (August, 2011) ... Language: English - ISBN-10: 0123813751 - ISBN-13: 978-0123813756

Introduction to Optimum Design 3e describes an organized approach to engineering design optimization in a rigorous yet simplified manner. It illustrates various concepts and procedures with simple examples and demonstrates their applicability to engineering design problems. Formulation of a design problem as an optimization problem is emphasized and illustrated throughout the text. Excel and MATLAB® are featured as learning and teaching aids. Basic concepts of optimality conditions and numerical methods are described with simple and practical examples, making the material highly teachable and learnable. + Includes applications of optimization methods for structural, mechanical, aerospace, and industrial engineering problems. + Introduction to MATLAB Optimization Toolbox. + Practical design examples introduce students to the use of optimization methods early in the book. + New example problems throughout the text are enhanced with detailed illustrations. + Optimum design with Excel Solver has been expanded into a full chapter. + New chapter on several advanced optimum design topics serves the needs of instructors who teach more advanced courses.

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