Global optimization based on metamodels using radial basis function with application to aircraft wings

2017 ◽  
Author(s):  
Nelson Diaz ◽  
Nelson Manzanares Filho ◽  
Edna da Silva
Keyword(s):  
Global Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Aircraft Wings ◽  
Radial Basis

Trust-region based adaptive radial basis function algorithm for global optimization of expensive constrained black-box problems

2021 ◽  
Vol 105 ◽  
pp. 107233
Author(s):  
Chengyang Liu ◽  
Zhiqiang Wan ◽  
Yijie Liu ◽  
Xuewu Li ◽  
Dianzi Liu
Keyword(s):  
Global Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Trust Region ◽  
Black Box ◽  
Radial Basis

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

2021 ◽  
Vol 15 ◽  
Author(s):  
Ziwu Ren ◽  
Rihui Li ◽  
Bin Chen ◽  
Hongmiao Zhang ◽  
Yuliang Ma ◽  
...  
Keyword(s):  
Global Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Receiver Operating Curve ◽  
Eeg Signals ◽  
Rbf Network ◽  
Network Parameters ◽  
Radial Basis ◽  
Driving Fatigue ◽  
Fatigue Detection

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.

Improved Strategies for Radial basis Function Methods for Global Optimization

2006 ◽  
Vol 37 (1) ◽  
pp. 113-135 ◽  
Author(s):  
Rommel G. Regis ◽  
Christine A. Shoemaker
Keyword(s):  
Global Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis
Sign in / Sign up

Export Citation Format

Share Document