A low cost and reliable Polak-Ribiere conjugate gradient deflation ICA algorithm for real signals

Ghasemi-Fard, M.; Ansari-Asl, Karim; Albera, Laurent; Kachenoura, Amar; Senhadji, Lotfi

doi:10.1109/iraniancee.2013.6599835

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…The Learning method of neural network was based on Polak-Ribiére Conjugate Gradient (PCG). The PCG Algorithm is faster and has higher quality of separation and a lower cost in computational than others (Ghasemi-Fard, 2013). The Combinations of these methods have not been done in the case of crude oil price forecasting previously.…”

Section: Introductionmentioning

confidence: 99%

The Application of EEMD and Neural Network Based on Polak-Ribiére Conjugate Gradient Algorithm for Crude Oil Prices Forecasting

Latif

Herawati

2016

MATEC Web of Conferences

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Forecasting crude oil prices is very difficult to do because it has nonlinear and nonstationary characteristics. This research proposes a crude oil prices forecasting using a combination of EEMD and neural network. EEMD was used to decompose the price of crude oil into several IMFs and one residue. Before the training and testing was processed using FNN, EEMD output is normalized to fulfill network activation function. Data pattern of neural network was determined based on the results of normalization. The Learning method of neural network was based on Polak-Ribiére Conjugate Gradient algorithm. The output of neural networks on each component IMFs and the residue was aggregated using Adaline. The last process is denormalization of the Adaline output. Output of denormalization is the end result of the crude oil price forecasting. After forecasting results has been known, it then compared with the results of several neural networks learning algorithm. The result shows that the proposed method has better forecasting ability. This is indicated by the error value which was smaller than other forecasting algorithms for crude oil price forecasting.

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Section: Introductionmentioning

confidence: 99%

The Application of EEMD and Neural Network Based on Polak-Ribiére Conjugate Gradient Algorithm for Crude Oil Prices Forecasting

Latif

Herawati

2016

MATEC Web of Conferences

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Forecasting tourist visits using data decomposition technique and learning optimization of artificial neural network

Herawati

Latif

2021

J. Phys.: Conf. Ser.

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Estimates of tourist visits is very important to determining policy and decision making. This study proposed a new method for forecasting tourist visits. A case study was conducted at a tourist spot in Sumenep, Indonesia. The model proposed is data decomposition and optimization of learning against tourist visits data. Data decomposed use the Ensemble Empirical Mode Decomposition (EEMD) method, then data learning use the Feedforward Neural Network (FNN) which was optimized using the Polak-Ribiere Conjugate Gradient (PCG). The two methods are integrated to produce accurate forecasts. Several patterns of learning data were carried out in this experiment. The results of this method show good permformance results as measured used RMSE and MSE.

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A low cost and reliable Polak-Ribiere conjugate gradient deflation ICA algorithm for real signals

Cited by 2 publications

References 12 publications

The Application of EEMD and Neural Network Based on Polak-Ribiére Conjugate Gradient Algorithm for Crude Oil Prices Forecasting

The Application of EEMD and Neural Network Based on Polak-Ribiére Conjugate Gradient Algorithm for Crude Oil Prices Forecasting

Forecasting tourist visits using data decomposition technique and learning optimization of artificial neural network

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