ANÁLISIS DE SERIES TEMPORALES MEDIANTE REDES NEURONALES. EJEMPLOS CON MATLAB PDF Download

Author: CESAR PEREZ LOPEZ
Publisher: CESAR PEREZ
ISBN:
Category : Mathematics
Languages : es
Pages : 307

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Book Description
MATLAB cuenta con la herramienta Deep Learning Toolbox que proporciona algoritmos, funciones y aplicaciones para crear, entrenar, visualizar y simular redes neuronales. Puede realizar clasificación, regresión, agrupamiento, reducción de dimensionalidad, pronóstico de series temporales y modelado y control de sistemas dinámicos. Las redes neuronales dinámicas son adecuadas para la predicción de series temporales. Puede utilizar la app Neural Net Time Series para resolver diferentes tipos de problemas de series temporales. Generalmente es mejor comenzar con la GUI y luego usarla para generar automáticamente scripts de línea de comandos. Antes de utilizar cualquiera de los métodos, el primer paso es definir el problema seleccionando un conjunto de datos. Cada GUI tiene acceso a muchos conjuntos de datos de muestra que puede utilizar para experimentar con la caja de herramientas. Si tiene un problema específico que desea resolver, puede cargar sus propios datos en el espacio de trabajo. Con MATLAB es posible resolver tres tipos diferentes de problemas de series temporales. En el primer tipo de problema de series de tiempo, se busca predecir valores futuros de una serie de tiempo y(t) a partir de valores pasados de esa serie de tiempo y valores pasados de una segunda serie de tiempo x(t). Esta forma de predicción se denomina red autorregresiva no lineal con entrada exógena (externa), o NARX. En el segundo tipo de problema de series temporales, sólo hay una serie involucrada. Los valores futuros de una serie temporal y(t) se predicen sólo a partir de valores pasados de esa serie. Esta forma de predicción se llama autorregresiva no lineal o NAR. El tercer problema de series de tiempo es similar al primer tipo, en el sentido de que están involucradas dos series, una serie de entrada (predictores) x(t) y una serie de salida (respuestas) y(t). Este libro desarrolla los métodos de predicción con series temporales a través de redes neuronales con MATLAB.

Author: Gopinath Rebala
Publisher: Springer
ISBN: 3030157296
Category : Technology & Engineering
Languages : en
Pages : 275

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Book Description
Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.

Author: Torsten P. Bohlin
Publisher: Springer Science & Business Media
ISBN: 1846284031
Category : Technology & Engineering
Languages : en
Pages : 363

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Book Description
This book reviews the theoretical fundamentals of grey-box identification and puts the spotlight on MoCaVa, a MATLAB-compatible software tool, for facilitating the procedure of effective grey-box identification. It demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. In addition, the book answers common questions which will help in building accurate models for systems with unknown inputs.

Author: Andreas S. Weigend
Publisher: Routledge
ISBN: 042997227X
Category : Social Science
Languages : en
Pages : 665

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Book Description
The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.

Author: Daniel J. Block
Publisher: Morgan & Claypool Publishers
ISBN: 1598291955
Category : Technology & Engineering
Languages : en
Pages : 112

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Book Description
This monograph describes the Reaction Wheel Pendulum, the newest inverted-pendulum-like device for control education and research. We discuss the history and background of the reaction wheel pendulum and other similar experimental devices. We develop mathematical models of the reaction wheel pendulum in depth, including linear and nonlinear models, and models of the sensors and actuators that are used for feedback control. We treat various aspects of the control problem, from linear control of themotor, to stabilization of the pendulum about an equilibrium configuration using linear control, to the nonlinear control problem of swingup control. We also discuss hybrid and switching control, which is useful for switching between the swingup and balance controllers. We also discuss important practical issues such as friction modeling and friction compensation, quantization of sensor signals, and saturation. This monograph can be used as a supplement for courses in feedback control at the undergraduate level, courses in mechatronics, or courses in linear and nonlinear state space control at the graduate level. It can also be used as a laboratory manual and as a reference for research in nonlinear control.

Author: Bryan Jennett
Publisher: Cambridge University Press
ISBN: 9780521441582
Category : Law
Languages : en
Pages : 246

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Book Description
A survey of the medical, ethical and legal issues that surround this controversial topic.

Author: Jorge Garza Ulloa
Publisher: Academic Press
ISBN: 0128125950
Category : Technology & Engineering
Languages : en
Pages : 664

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Book Description
Applied Biomechatronics Using Mathematical Models provides an appropriate methodology to detect and measure diseases and injuries relating to human kinematics and kinetics. It features mathematical models that, when applied to engineering principles and techniques in the medical field, can be used in assistive devices that work with bodily signals. The use of data in the kinematics and kinetics analysis of the human body, including musculoskeletal kinetics and joints and their relationship to the central nervous system (CNS) is covered, helping users understand how the complex network of symbiotic systems in the skeletal and muscular system work together to allow movement controlled by the CNS. With the use of appropriate electronic sensors at specific areas connected to bio-instruments, we can obtain enough information to create a mathematical model for assistive devices by analyzing the kinematics and kinetics of the human body. The mathematical models developed in this book can provide more effective devices for use in aiding and improving the function of the body in relation to a variety of injuries and diseases. - Focuses on the mathematical modeling of human kinematics and kinetics - Teaches users how to obtain faster results with these mathematical models - Includes a companion website with additional content that presents MATLAB examples

Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
ISBN: 1783555149
Category : Computers
Languages : en
Pages : 455

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Book Description
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Category : Medical
Languages : en
Pages : 595

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Book Description
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Author: Bernhard Baune
Publisher: Academic Press
ISBN: 0128110740
Category : Medical
Languages : en
Pages : 638

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Book Description
Inflammation and Immunity in Depression: Basic Science and Clinical Applications is the first book to move beyond the established theory of cytokine-induced depression and explore the broader role the immune system plays in this devastating mood disorder. The book fully explores the most recent lines of research into this rapidly advancing field, including alterations of T-cells, the neurobiological implications of neuroinflammation and immune alterations for brain development and function, and the genetic components of neuroinflammation in depression, including the relationships between stress and inflammation that are revealing gene-environment interactions in the disorder. Combining contributions from researchers worldwide, this book provides the most comprehensive discussion available today on the involvement of the innate immune and adaptive immune systems in depressive disorder. Chapters span neuroscience, psychology, clinical applications and future directions, making this book an invaluable resource for advanced students, researchers and practitioners who need to understand the complex and varied role of inflammation and immune responses in depression. - Synthesizes current knowledge of inflammation and immunity in depression, ranging from basic neuroscience research, to clinical applications in psychiatry - Expands on the long-established theory of cytokine-induced depression to discuss broader involvement of the immune system - Explores translational potential of targeting immune dysfunction for clinical interventions