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General Type-2 Fuzzy Edge Detection in the Preprocessing of a Face Recognition System Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Particle Swarm Optimization of Ensemble Neural Networks with Type-1 and Type-2 Fuzzy Integration for the Taiwan Stock Exchange

Authors : Martha Pulido, Patricia Melin, Olivia Mendoza

Published in: Nature-Inspired Design of Hybrid Intelligent Systems

Publisher: Springer International Publishing

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Abstract

This paper describes an optimization method based on particle swarm optimization (PSO) for ensemble neural networks with type-1 and type-2 fuzzy aggregation for forecasting complex time series. The time series that was considered in this paper to compare the hybrid approach with traditional methods is the Taiwan Stock Exchange (TAIEX), and the results shown are for the optimization of the structure of the ensemble neural network with type-1 and type-2 fuzzy integration. Simulation results show that ensemble approach produces good prediction of the Taiwan Stock Exchange.

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previous chapter Gravitational Search Algorithm with Parameter Adaptation Through a Fuzzy Logic System
next chapter A New Hybrid PSO Method Applied to Benchmark Functions
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Metadata
Title
Particle Swarm Optimization of Ensemble Neural Networks with Type-1 and Type-2 Fuzzy Integration for the Taiwan Stock Exchange
Authors
Martha Pulido
Patricia Melin
Olivia Mendoza
Copyright Year
2017
Book
Nature-Inspired Design of Hybrid Intelligent Systems

Print ISBN: 978-3-319-47053-5

Electronic ISBN: 978-3-319-47054-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47054-2_27

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