Evolving RBF neural networks for time-series forecasting with EvRBF

Rivas, Víctor M.; Merelo, J. J.; Valdivieso, Pedro Ángel Castillo; Arenas, M. G.; Castellano, Javier G.

doi:10.1016/j.ins.2003.09.025

Cited by 93 publications

(43 citation statements)

References 19 publications

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“…The work by Arizmendi et al (1993) obtained accurate predictions of the airborne pollen concentrations using ANNs. Hu (1998) employed ANNs, andRivas et al (2004) RBFNs, for forecasting British pound and US dollar exchange rates. Bezerianos et al (1999) employed RBFNs for the assessment and prediction of the heart rate variability.…”

Section: Soft Computing Methods For Time Series Forecastingmentioning

confidence: 99%

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Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm

et al. 2011

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This paper introduces Lags COevolving with Rbfns (L-Co-R), a coevolutionary method developed to face time-series forecasting problems. L-Co-R simultaneously evolves the model that provides the forecasted values and the set of time lags the model must use in the prediction process. Coevolution takes place by means of two populations that evolve at the same time, cooperating between them; the first population is composed of radial basis function neural networks; the second one contains the individuals representing the sets of lags. Thus, the final solution provided by the method comprises both the neural net and the set of lags that better approximate the time series. The method has been tested across 34 different time series datasets, and the results compared to 6 different methods referenced in literature, and with respect to 4 different error measures. The results show that L-Co-R outperforms the rest of methods, as the statistical analysis carried out indicates.

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Section: Soft Computing Methods For Time Series Forecastingmentioning

confidence: 99%

“…• EvRBF (Evolutionary Radial Basis Function Neural Networks, Rivas et al 2004). This method is focused on determining the parameters of RBFNs (number of neurons, and their respective centers and radii) automatically.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm

et al. 2011

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“…Afterwards, the weighted sum is calculated by the output layer neurons as a function of the outputs of hidden layer neurons and the weights of the links connecting them to the output layer neurons. Evolving Radial Basis Function Neural Networks or Ev-RBF [19] make classification process simpler by automatically determining the values of RBF-NN parameters using evolutionary algorithms.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Application of evolutionary algorithms in detection of SIP based flooding attacks

Akbar

Farooq

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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The Session Initiation Protocol (SIP) is the de facto standard for user's session control in the next generation Voice over Internet Protocol (VoIP) networks based on the IP Multimedia Subsystem (IMS) framework. In this paper, we first analyze the role of SIP based floods in the Denial of Service (DoS) attacks on the IMS. Afterwards, we present an online intrusion detection framework for detection of such attacks. We analyze the role of different evolutionary and non-evolutionary classifiers on the classification accuracy of the proposed framework. We have evaluated the performance of our intrusion detection framework on a traffic in which SIP floods of varying intensities are injected. The results of our study show that the evolutionary classifiers like sUpervised Classifier System (UCS) and Genetic clASSIfier sySTem (GAssist) can even detect low intensity SIP floods in realtime. Finally, we formulate a set of specific guidelines that can help VoIP service providers in customizing our intrusion detection framework by selecting an appropriate classifier-depending on their requirements in different service scenarios.

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“…Radial basis function (RBF) neural networks were introduced by Broomhead and Lowe (1988) and were mainly used for function approximation, time series forecasting, as well as classification. This approach has been used to model very complex and nonlinear phenomena in engineering, hydrology, and economics (Yao et al 2001, Dibike and Solomatlne 2001, Rivas et al 2004, Morelli et al 2004. We explored the predictive ability of the RBF neural network approach using historical records-from the River Darling, Australia.…”

Section: Introductionmentioning

confidence: 99%