Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work compares seven regression algorithms implemented in artificial neural networks (ANNs) supported by 14 por-quality features, which are based in higher-order statistics. Combining time and frequency domain estimators to deal with non-stationary measurement sequences, the final goal of the system is the implementation in the future smart grid to guarantee compatibility beten all equipment connected. The principal results are based in spectral kurtosis measurements, which easily adapt to the impulsive nature of the por quality events. These results verify that the proposed technique is capable of offering interesting results for por quality (PQ) disturbance classification. The best results are obtained using radial basis networks, generalized regression, and multilayer perceptron, mainly due to the non-linear nature of data.
['José Carlos Palomares Salas', 'Juan José González de la Rosa', 'José María Sierra Fernández', 'Agustín Pérez']
I am writing a research paper on a idea HOS network-based classification of power quality events via regression algorithms
[{"from": "human", "value": "I am writing a research paper on a idea HOS network-based classification of power quality events via regression algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work compares seven regression algorithms implemented in artificial neural networks (ANNs) supported by 14 por-quality features, which are based in higher-order statistics. Combining time and frequency domain estimators to deal with non-stationary measurement sequences, the final goal of the system is the implementation in the future smart grid to guarantee compatibility beten all equipment connected. The principal results are based in spectral kurtosis measurements, which easily adapt to the impulsive nature of the por quality events. These results verify that the proposed technique is capable of offering interesting results for por quality (PQ) disturbance classification. The best results are obtained using radial basis networks, generalized regression, and multilayer perceptron, mainly due to the non-linear nature of data."}]