Time Series Forecasting Using Neural Networks Examples With Matlab

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TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB

Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
ISBN 13 :
Total Pages : 283 pages
Book Rating : 4./5 ( download)

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Book Synopsis TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB by : Cesar Perez Lopez

Download or read book TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Deep Leraning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, timeseries forecasting, and dynamic system modeling and control. Dynamic neural networks are good at timeseries prediction. You can use the Neural Net Time Series app to solve different kinds of time series problems It is generally best to start with the GUI, and then to use the GUI to automatically generate command line scripts. Before using either method, the first step is to define the problem by selecting a data set. Each GUI has access to many sample data sets that you can use to experiment with the toolbox. If you have a specific problem that you want to solve, you can load your own data into the workspace. With MATLAB is possibe to solve three different kinds of time series problems. In the first type of time series problem, you would like to predict future values of a time series y(t) from past values of that time series and past values of a second time series x(t). This form of prediction is called nonlinear autoregressive network with exogenous (external) input, or NARX. In the second type of time series problem, there is only one series involved. The future values of a time series y(t) are predicted only from past values of that series. This form of prediction is called nonlinear autoregressive, or NAR. The third time series problem is similar to the first type, in that two series are involved, an input series (predictors) x(t) and an output series (responses) y(t). Here you want to predict values of y(t) from previous values of x(t), but without knowledge of previous values of y(t). This book develops methods for time series forecasting using neural networks across MATLAB

Deep Learning for Time Series Forecasting

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 572 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning for Time Series Forecasting by : Jason Brownlee

Download or read book Deep Learning for Time Series Forecasting written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-08-30 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.

MATLAB Deep Learning

Author : Phil Kim
Publisher : Apress
ISBN 13 : 1484228456
Total Pages : 162 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis MATLAB Deep Learning by : Phil Kim

Download or read book MATLAB Deep Learning written by Phil Kim and published by Apress. This book was released on 2017-06-15 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

Neural Networks in Finance

Author : Paul D. McNelis
Publisher : Academic Press
ISBN 13 : 0124859674
Total Pages : 262 pages
Book Rating : 4.1/5 (248 download)

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Book Synopsis Neural Networks in Finance by : Paul D. McNelis

Download or read book Neural Networks in Finance written by Paul D. McNelis and published by Academic Press. This book was released on 2005-01-05 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website

Complex-Valued Neural Networks with Multi-Valued Neurons

Author : Igor Aizenberg
Publisher : Springer Science & Business Media
ISBN 13 : 3642203523
Total Pages : 273 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Complex-Valued Neural Networks with Multi-Valued Neurons by : Igor Aizenberg

Download or read book Complex-Valued Neural Networks with Multi-Valued Neurons written by Igor Aizenberg and published by Springer Science & Business Media. This book was released on 2011-06-24 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information. These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.

DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES

Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
ISBN 13 : 1716584841
Total Pages : 154 pages
Book Rating : 4.7/5 (165 download)

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Book Synopsis DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES by : Cesar Perez Lopez

Download or read book DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on 2020-09-13 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Deep Learning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets (Big data), you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.

ARTIFICIAL INTELLIGENCE ALGORITHMS FOR UNSUPERVISED LEARNING: CLUSTERING AND PATTERN RECOGNITION WITH NEURAL NETWORKS. Examples with MATLAB

Download ARTIFICIAL INTELLIGENCE ALGORITHMS FOR UNSUPERVISED LEARNING: CLUSTERING AND PATTERN RECOGNITION WITH NEURAL NETWORKS. Examples with MATLAB PDF Online Free

Author : CESAR PERZ LOPEZ
Publisher : SCIENTIFIC BOOKS
ISBN 13 :
Total Pages : 200 pages
Book Rating : 4./5 ( download)

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Book Synopsis ARTIFICIAL INTELLIGENCE ALGORITHMS FOR UNSUPERVISED LEARNING: CLUSTERING AND PATTERN RECOGNITION WITH NEURAL NETWORKS. Examples with MATLAB by : CESAR PERZ LOPEZ

Download or read book ARTIFICIAL INTELLIGENCE ALGORITHMS FOR UNSUPERVISED LEARNING: CLUSTERING AND PATTERN RECOGNITION WITH NEURAL NETWORKS. Examples with MATLAB written by CESAR PERZ LOPEZ and published by SCIENTIFIC BOOKS. This book was released on with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence combines mathematical algorithms and techniques from Machine Learning, Deep Learning and Big Data to extract the knowledge contained in the data and present it in an understandable and automatic way. Neural networks and their applications are a fundamental tool to develop work in Artificial Intelligence. On the other hand, unsupervised learning is more closely aligned with Artificial Intelligence as it gives the idea that a machine can learn to identify complex processes and patterns without the need for a human to provide guidance and supervision throughout the learning process. Some examples of unsupervised learning algorithms include clustering and association rules. In the case of this type of learning, there is no pre-training data set; the problem is approached blindly and only with logical operations to guide it. Although at first glance it seems impossible, it is about the ability to solve complex problems using only input data and logical algorithms. This avoids the use of reference data. Unsupervised learning algorithms are used to discover hidden patterns in unlabeled data. Unlike supervised learning algorithms, where there is prior knowledge of the desired answers, these algorithms do not have a set of ordered data. They are responsible for determining the most important common characteristics of a group of information and then grouping them according to their similarities. Among the most interesting models are the neural networks. MATLAB implementrs the Deep Learning Toolbox specialized in the techniques of analytics based on neural networks. Throughout this book the techniques of analytics for clustering and classification based on neural networks are developed using MATLAB software

Artificial Neural Networks - ICANN 2006

Author : Stefanos Kollias
Publisher : Springer
ISBN 13 : 3540386270
Total Pages : 1041 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Artificial Neural Networks - ICANN 2006 by : Stefanos Kollias

Download or read book Artificial Neural Networks - ICANN 2006 written by Stefanos Kollias and published by Springer. This book was released on 2006-09-01 with total page 1041 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 4131 and LNCS 4132 constitutes the refereed proceedings of the 16th International Conference on Artificial Neural Networks, ICANN 2006. The set presents 208 revised full papers, carefully reviewed and selected from 475 submissions. This first volume presents 103 papers, organized in topical sections on feature selection and dimension reduction for regression, learning algorithms, advances in neural network learning methods, ensemble learning, hybrid architectures, and more.

Theory and Applications of Time Series Analysis

Author : Olga Valenzuela
Publisher : Springer Nature
ISBN 13 : 3030260364
Total Pages : 376 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Theory and Applications of Time Series Analysis by : Olga Valenzuela

Download or read book Theory and Applications of Time Series Analysis written by Olga Valenzuela and published by Springer Nature. This book was released on 2019-10-18 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected peer-reviewed contributions from the International Conference on Time Series and Forecasting, ITISE 2018, held in Granada, Spain, on September 19-21, 2018. The first three parts of the book focus on the theory of time series analysis and forecasting, and discuss statistical methods, modern computational intelligence methodologies, econometric models, financial forecasting, and risk analysis. In turn, the last three parts are dedicated to applied topics and include papers on time series analysis in the earth sciences, energy time series forecasting, and time series analysis and prediction in other real-world problems. The book offers readers valuable insights into the different aspects of time series analysis and forecasting, allowing them to benefit both from its sophisticated and powerful theory, and from its practical applications, which address real-world problems in a range of disciplines. The ITISE conference series provides a valuable forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing computer science, mathematics, statistics and econometrics.

Analyzing Neural Time Series Data

Author : Mike X Cohen
Publisher : MIT Press
ISBN 13 : 0262019876
Total Pages : 615 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Analyzing Neural Time Series Data by : Mike X Cohen

Download or read book Analyzing Neural Time Series Data written by Mike X Cohen and published by MIT Press. This book was released on 2014-01-17 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.

MATLAB for Machine Learning

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
ISBN 13 : 1835089534
Total Pages : 374 pages
Book Rating : 4.8/5 (35 download)

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Book Synopsis MATLAB for Machine Learning by : Giuseppe Ciaburro

Download or read book MATLAB for Machine Learning written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2024-01-30 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applications Key Features Work with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithms Evaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoring Uncover effective approaches to deep learning for computer vision, time series analysis, and forecasting Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.What you will learn Discover different ways to transform data into valuable insights Explore the different types of regression techniques Grasp the basics of classification through Naive Bayes and decision trees Use clustering to group data based on similarity measures Perform data fitting, pattern recognition, and cluster analysis Implement feature selection and extraction for dimensionality reduction Harness MATLAB tools for deep learning exploration Who this book is for This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.

Enhanced Bayesian Network Models for Spatial Time Series Prediction

Author : Monidipa Das
Publisher : Springer Nature
ISBN 13 : 3030277496
Total Pages : 168 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Enhanced Bayesian Network Models for Spatial Time Series Prediction by : Monidipa Das

Download or read book Enhanced Bayesian Network Models for Spatial Time Series Prediction written by Monidipa Das and published by Springer Nature. This book was released on 2019-11-07 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

Advances in Artificial Intelligence - IBERAMIA 2002

Author : Francisco J. Garijo
Publisher : Springer Science & Business Media
ISBN 13 : 354000131X
Total Pages : 974 pages
Book Rating : 4.5/5 (4 download)

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Book Synopsis Advances in Artificial Intelligence - IBERAMIA 2002 by : Francisco J. Garijo

Download or read book Advances in Artificial Intelligence - IBERAMIA 2002 written by Francisco J. Garijo and published by Springer Science & Business Media. This book was released on 2002-10-30 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2002, held in Seville, Spain, in November 2002. The 97 revised full papers presented were carefully reviewed and selected from a total of 345 submissions. The papers are organized in topical sections on knowledge representation and reasoning, machine learning, uncertainty and fuzzy systems, genetic algorithms, neural nets, distributed artificial intelligence and multi-agent systems, natural language processing, intelligent tutoring systems, control and real time, robotics, and computer vision.

Handbook of Drought and Water Scarcity

Author : Saeid Eslamian
Publisher : CRC Press
ISBN 13 : 1315404206
Total Pages : 750 pages
Book Rating : 4.3/5 (154 download)

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Book Synopsis Handbook of Drought and Water Scarcity by : Saeid Eslamian

Download or read book Handbook of Drought and Water Scarcity written by Saeid Eslamian and published by CRC Press. This book was released on 2017-08-02 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume include over 30 chapters, written by experts from around the world. It examines drought and all of the fundamental principles relating to drought and water scarcity. It includes coverage of the causes of drought, occurences, preparations, drought vulnerability assessments, societal implications, and more.

Data Analytics for Management, Banking and Finance

Author : Foued Saâdaoui
Publisher : Springer Nature
ISBN 13 : 3031365704
Total Pages : 338 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Data Analytics for Management, Banking and Finance by : Foued Saâdaoui

Download or read book Data Analytics for Management, Banking and Finance written by Foued Saâdaoui and published by Springer Nature. This book was released on 2023-09-19 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance. It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies. The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks

Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering

Author : Shahab Araghinejad
Publisher : Springer Science & Business Media
ISBN 13 : 9400775067
Total Pages : 299 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering by : Shahab Araghinejad

Download or read book Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering written by Shahab Araghinejad and published by Springer Science & Business Media. This book was released on 2013-11-26 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.

Feedforward Neural Network Methodology

Author : Terrence L. Fine
Publisher : Springer Science & Business Media
ISBN 13 : 0387226494
Total Pages : 353 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Feedforward Neural Network Methodology by : Terrence L. Fine

Download or read book Feedforward Neural Network Methodology written by Terrence L. Fine and published by Springer Science & Business Media. This book was released on 2006-04-06 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.