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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

9. Time Series Prediction Using Coalitions and Self-organizing Maps

verfasst von : Juan C. Burguillo, Juan García-Rois

Erschienen in: Self-organizing Coalitions for Managing Complexity

Verlag: Springer International Publishing

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Abstract

In this chapter we consider the Time Series Prediction problem (TSP), and the use of Self-organizing Maps (SOM) as the basic neural network model to apply. We conduct a topology-based TSP performance analysis, based on extensive numerical simulations, taking into account different complex networks for connecting the neurons within the SOM. We introduce the use of coalitions, and a parameter free version of our Coalitional Algorithm for SOM (CASOM), which adapts the neuron neighborhood to the time series under analysis by means of dynamic coalitions. The results obtained by CASOM are better than the ones provided by SOM in all the topologies and time series considered in this chapter, even when the network structure changes along the training process. Besides, and more remarkable, the number of training epochs, needed by CASOM to stabilize the weights of its neurons, is much lower than SOM.

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Vorheriges Kapitel Optimization Models with Coalitional Cellular Automata
Nächstes Kapitel Coalitions of Electric Vehicles in Smart Grids
Fußnoten
1
In the original Watts–Strogatz model, p stands for the rewiring probability of an existing edge, while p in [11, 18] refers to the probability of adding a new link.
 
2
Except in the MGTS benchmark, although it reaches the best SP value when \(P_u=0.25\).
 
3
In fact, a traditional and very successful combination of Cellular Automata and Artificial Neural Networks (similar to SOM) are Cellular Neural Network models [7].
 
4
Infection means here that coalitions try to join nearby neurons. We will also see an alternative joining method, where neurons are the ones that request to join coalitions.
 
5
Note that in the first part of this chapter, related to SOM, it was constantly decreased after every sample, i.e., \(\triangle \alpha = \frac{1}{T_s . T_e}\).
 
6
Note that all coalition leaders have been BMUs during a training epoch, but not all BMUs are coalition leaders as their coalition members could have been stolen by other BMUs.
 
7
Note that this is a difference with [6], where the average MAE (Mean Absolute Error) was considered.
 
8
Remember that for each run a new network is generated.
 
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Metadaten
Titel
Time Series Prediction Using Coalitions and Self-organizing Maps
verfasst von
Juan C. Burguillo
Juan García-Rois
Copyright-Jahr
2018
Buch
Self-organizing Coalitions for Managing Complexity

Print ISBN: 978-3-319-69896-0

Electronic ISBN: 978-3-319-69898-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-69898-4_9