Interval Type-2 Fuzzy Logic System for Fuzzy Time Series Forecasting
Title | Interval Type-2 Fuzzy Logic System for Fuzzy Time Series Forecasting PDF eBook |
Author | |
Publisher | |
Pages | |
Release | 2015 |
Genre | |
ISBN |
Advances in Time Series Forecasting
Title | Advances in Time Series Forecasting PDF eBook |
Author | Cagdas Hakan Aladag |
Publisher | Bentham Science Publishers |
Pages | 196 |
Release | 2017-12-06 |
Genre | Mathematics |
ISBN | 1681085283 |
This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting frameworks are discussed and their application is demonstrated. The second volume of the series includes applications of some powerful forecasting approaches with a focus on fuzzy time series methods. Chapters integrate these methods with concepts such as neural networks, high order multivariate systems, deterministic trends, distance measurement and much more. The chapters are contributed by eminent scholars and serve to motivate and accelerate future progress while introducing new branches of time series forecasting. This book is a valuable resource for MSc and PhD students, academic personnel and researchers seeking updated and critically important information on the concepts of advanced time series forecasting and its applications.
Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction
Title | Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction PDF eBook |
Author | Jesus Soto |
Publisher | Springer |
Pages | 103 |
Release | 2017-11-19 |
Genre | Technology & Engineering |
ISBN | 3319712640 |
This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.
Uncertain Rule-Based Fuzzy Systems
Title | Uncertain Rule-Based Fuzzy Systems PDF eBook |
Author | Jerry M. Mendel |
Publisher | Springer |
Pages | 701 |
Release | 2017-05-17 |
Genre | Technology & Engineering |
ISBN | 3319513702 |
The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material.
Pattern Recognition with Fuzzy Objective Function Algorithms
Title | Pattern Recognition with Fuzzy Objective Function Algorithms PDF eBook |
Author | James C. Bezdek |
Publisher | Springer Science & Business Media |
Pages | 267 |
Release | 2013-03-13 |
Genre | Mathematics |
ISBN | 147570450X |
The fuzzy set was conceived as a result of an attempt to come to grips with the problem of pattern recognition in the context of imprecisely defined categories. In such cases, the belonging of an object to a class is a matter of degree, as is the question of whether or not a group of objects form a cluster. A pioneering application of the theory of fuzzy sets to cluster analysis was made in 1969 by Ruspini. It was not until 1973, however, when the appearance of the work by Dunn and Bezdek on the Fuzzy ISODATA (or fuzzy c-means) algorithms became a landmark in the theory of cluster analysis, that the relevance of the theory of fuzzy sets to cluster analysis and pattern recognition became clearly established. Since then, the theory of fuzzy clustering has developed rapidly and fruitfully, with the author of the present monograph contributing a major share of what we know today. In their seminal work, Bezdek and Dunn have introduced the basic idea of determining the fuzzy clusters by minimizing an appropriately defined functional, and have derived iterative algorithms for computing the membership functions for the clusters in question. The important issue of convergence of such algorithms has become much better understood as a result of recent work which is described in the monograph.
Fuzzy Sets and Their Extensions: Representation, Aggregation and Models
Title | Fuzzy Sets and Their Extensions: Representation, Aggregation and Models PDF eBook |
Author | Humberto Bustince |
Publisher | Springer |
Pages | 674 |
Release | 2007-10-30 |
Genre | Computers |
ISBN | 3540737235 |
This carefully edited book presents an up-to-date state of current research in the use of fuzzy sets and their extensions. It pays particular attention to foundation issues and to their application to four important areas where fuzzy sets are seen to be an important tool for modeling and solving problems. The book’s 34 chapters deal with the subject with clarity and effectiveness. They include four review papers introducing some non-standard representations
Type-2 Fuzzy Logic: Theory and Applications
Title | Type-2 Fuzzy Logic: Theory and Applications PDF eBook |
Author | Oscar Castillo |
Publisher | Springer Science & Business Media |
Pages | 252 |
Release | 2008-02-20 |
Genre | Mathematics |
ISBN | 3540762833 |
This book describes new methods for building intelligent systems using type-2 fuzzy logic and soft computing (SC) techniques. The authors extend the use of fuzzy logic to a higher order, which is called type-2 fuzzy logic. Combining type-2 fuzzy logic with traditional SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers. This book is intended to be a major reference tool and can be used as a textbook.