Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction / by Jesus Soto, Patricia Melin, Oscar Castillo.
By: Soto, Jesus, autor
Contributor(s): Melin, Patricia, autor
| Castillo, Oscar, autor
Material type: 
E-bookSeries: (SpringerBriefs in Computational Intelligence, 2625-3704); (Engineering (Springer-11647)).Publisher: Cham : Springer International Publishing, 2018Description: 1 recurso en línea (VIII, 97 páginas 101 ilustraciones, 73 ilustraciones a color).ISBN: 9783319712642.Subject: Series temporales | AlgoritmosOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)
Abstract: 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. .
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
LIBRO-E NO PRÉSTAMO
|
Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | QA280 .S686 2018 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook.15112905 |
Browsing Madrid Digital shelves, Shelving location: Acceso Electrónico (UEM) Close shelf browser (Hides shelf browser)
| QA280 .B738 2016 EB Studies on Time Series Applications in Environmental Sciences | QA280 .K663 2017 EB Time-series prediction and applications : a machine intelligence approach | QA280 .R933 2016 EB Compression-Based Methods of Statistical Analysis and Prediction of Time Series | QA280 .S686 2018 EB Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction | QA280 .Z436 2017 EB Time series analysis methods and applications for flight data | QA292 2020 EB An Introduction to Numerical Methods for the Physical Sciences | QA295 2014 EB Selected Asymptotic Methods with Applications to Electromagnetics and Antennas |
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. .
There are no comments on this title.