Extremely short-term wind speed prediction based on RSCMAC

Chiang, Ching-Tsan; Lu, Wen-Lung; Jhuang, Hao-An

doi:10.1109/ascc.2013.6606189

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…For example, in one of the recent studies, CMAC was adapted to construct fall detection algorithm in a smart omni-directional mobile robot which was proposed to assist the elderly [16]. Besides that, CMAC was also applied in the development of extremely short term wind power forecasting system [17]. In a study of solar energy heliostat fields, CMAC was proposed to automatically fault diagnosis within the fields [18].…”

Section: Cerebellar Model Articulation Controllermentioning

confidence: 99%

Extracting Features Using Computational Cerebellar Model for Emotion Classification

Yaacob

Wahab

Kamaruddin³

2013

2013 International Conference on Advanced Computer Science Applications and Technologies

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Several feature extraction techniques have been employed to extract features from EEG signals for classifying emotions. Such techniques are not constructed based on the understanding of EEG and brain functions, neither inspired by the understanding of emotional dynamics. Hence, the features are difficult to be interpreted and yield low classification performance. In this study, a new feature extraction technique using Cerebellar Model Articulation Controller (CMAC) is proposed. The features are extracted from the weights of data-driven self-organizing feature map that are adjusted during training to optimize the error obtained from the desired output and the calculated output.Multi-Layer Perceptron (MLP) classifier is then employed to perform classification on fear, happiness, sadness and calm emotions. Experimental results show that the average accuracy of classifying emotions from EEG signals captured on 12 children aged between 4 to 6 years old ranging from 84.18% to 89.29%. In addition, classification performance for features derived from other techniques such as Power Spectrum Density (PSD), Kernel Density Estimation (KDE) and Mel-Frequency Cepstral Coefficients (MFCC) are also presented as a standard benchmark for comparison purpose. It is observed that the proposed approach is able to yield accuracy of 33.77% to 55% as compared to the respective comparison features. The experimental results indicated that the proposed approach has potential for comparative emotion recognition accuracy when coupled with MLP.

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Section: Cerebellar Model Articulation Controllermentioning

confidence: 99%

Extracting Features Using Computational Cerebellar Model for Emotion Classification

Yaacob

Wahab

Kamaruddin³

2013

2013 International Conference on Advanced Computer Science Applications and Technologies

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Data-Driven Construction of Local Models for Short-Term Wind Speed Prediction

Salas

2015

Advances in Artificial Intelligence and Its Applications

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CMAC-based Computational Model of Affects (CCMA) for profiling emotion from EEG signals

Yaacob

Wahab

Shaikhli

et al. 2014

The 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M)

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Several studies have been perfo emotions using EEG signals through affe approach. It includes data acquisition, signa feature extraction and classification. Differen feature extraction and classification techn proposed. However, the results are subjective include subject-independent classification. In profiling model, known as CMAC-based Com of Affects (CCMA), is proposed. ), CMAC is reasonable model for processing EEG signa capabilities to solve non-linear problem organization feature mapping (SOFM). F extracted using CCMA are trained using Neural Network (EFuNN) as the classifier. classification of emotions using features that power spectral density (PSD) was also perfor shows that the performance of using CCM emotions outperforms the performance of cla from PSD features. Keywords-CMAC; EEG; affective com feature extractionI.

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Extremely short-term wind speed prediction based on RSCMAC

Cited by 3 publications

References 9 publications

Extracting Features Using Computational Cerebellar Model for Emotion Classification

Extracting Features Using Computational Cerebellar Model for Emotion Classification

Data-Driven Construction of Local Models for Short-Term Wind Speed Prediction

CMAC-based Computational Model of Affects (CCMA) for profiling emotion from EEG signals

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