Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

Petković, Dalibor; Gocić, Milan; Shamshirband, Shahaboddin; Qasem, Sultan Noman; Trajković, Slaviša

doi:10.1007/s00704-015-1522-y

Cited by 47 publications

(15 citation statements)

References 81 publications

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“…The RBFNN consists of one input layer, one hidden layer with a nonlinear RBF activation function, and one output layer with entirely different roles [23][24][25]. From the input layer to the hidden layer, a Gaussian transfer function is used for the hidden neurons, so the transformation is nonlinear; it can be expressed as follows:…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

Chen

Wei

et al. 2017

Water

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Medium-and long-term runoff forecasting is essential for hydropower generation and water resources coordinated regulation in the Yellow River headwaters region. Climate change has a great impact on runoff within basins, and incorporating different climate information into runoff forecasting can assist in creating longer lead-times in planning periods. In this paper, a multimodel approach was developed to further improve the accuracy and reliability of runoff forecasting fully considering of large-scale and local-scale climatic factors. First, with four large-scale atmospheric oscillations, sea surface temperature, precipitation, and temperature as the predictors, multiple linear regression (MLR), radial basis function neural network (RBFNN), and support vector regression (SVR) models were built. Next, a Bayesian model averaging (BMA)-based multimodel was developed using weighted MLR, RBFNN, and SVR models, and the performance of the BMA-based multimodel was compared to those of the MLR, RBFNN, and SVR models. Finally, the high-runoff performance of these four models was further analyzed to prove the effectiveness of each model. The BMA-based multimodel performed better than those of the other models, as well as high-runoff forecasting. The results also revealed that the performance of the forecasting models with multiple climatic factors were generally superior to that without climatic factors. The BMA-based multimodel with climatic factors not only provides a promising, reliable method for medium-and long-term runoff forecasting, but also facilitates uncertainty estimation under different confidence intervals.

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Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

Chen

Wei

et al. 2017

Water

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“…According to [ 30 ], the basis function can be defined in several ways, while some of the most commonly used basis functions are as follows: Gaussian, multi-quadric, inverse multi-quadric, generalised inverse multi-quadric, thin plate spline, cubic and linear function. In this study, the RBF is represented by the Gaussian function that acts as the activation function for the neurons in the hidden layer formed by every term δ k .…”

Section: Radial Basis Function Neural Network (Rbf Nn): An Overviewmentioning

confidence: 99%

“…To improve the diversity of individuals results in higher chance to search in the direction of global optimal [ 29 ], proposes an integrated hybrid method with PSO and GA for RBFNN training. Similarly [ 30 – 32 ], proposes a PSO based training for RBF NN for diverse applications. [ 33 ] presents a spatial correlation model algorithm for training ANN for wind speed and power forecasting.…”

Section: Introductionmentioning

confidence: 99%

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

Victoire

2018

PLoS ONE

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This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.

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“…The performance of the RBF network heavily relies on network parameters, which should be optimized globally for best performance. The RBF network parameters can be estimated using the existing global optimization methods (Petković et al, 2016 ; Aljarah et al, 2018 ). Unfortunately, due to a relatively large number of network parameters that need to be optimized, the existing global optimization methods show high computational cost and slow convergence and further lead to low classification accuracy and efficiency of the RBF network.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function

Ren¹,

Li²,

Chen³

et al. 2021

Front. Neurorobot.

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Electroencephalography (EEG)-based driving fatigue detection has gained increasing attention recently due to the non-invasive, low-cost, and potable nature of the EEG technology, but it is still challenging to extract informative features from noisy EEG signals for driving fatigue detection. Radial basis function (RBF) neural network has drawn lots of attention as a promising classifier due to its linear-in-the-parameters network structure, strong non-linear approximation ability, and desired generalization property. The RBF network performance heavily relies on network parameters such as the number of the hidden nodes, number of the center vectors, width, and output weights. However, global optimization methods that directly optimize all the network parameters often result in high evaluation cost and slow convergence. To enhance the accuracy and efficiency of EEG-based driving fatigue detection model, this study aims to develop a two-level learning hierarchy RBF network (RBF-TLLH) which allows for global optimization of the key network parameters. Experimental EEG data were collected, at both fatigue and alert states, from six healthy participants in a simulated driving environment. Principal component analysis was first utilized to extract features from EEG signals, and the proposed RBF-TLLH was then employed for driving status (fatigue vs. alert) classification. The results demonstrated that the proposed RBF-TLLH approach achieved a better classification performance (mean accuracy: 92.71%; area under the receiver operating curve: 0.9199) compared to other widely used artificial neural networks. Moreover, only three core parameters need to be determined using the training datasets in the proposed RBF-TLLH classifier, which increases its reliability and applicability. The findings demonstrate that the proposed RBF-TLLH approach can be used as a promising framework for reliable EEG-based driving fatigue detection.

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Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

Cited by 47 publications

References 81 publications

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function

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