Speech and musical note signals have overlapping spectra. Hence, extraction of the speech signal cannot be done by using conventional filters. The new approach is to employ a type of artificial neural network (ANN) called the General Regression Neural Network (GRNN). The GRNN does not need any information from the frequency domain – it learns to map an input signal to the desired output signal ...

As constantes de velocidade da reação do ozônio com 95 alcenos (-logkO3) e do radical hidroxila (•OH) com 98 alcenos (-logkOH) na atmosfera foram previstas por modelos de relações quantitativas entre estrutura e atividade (QSAR). Cálculos usando a teoria do funcional da densidade (DFT) foram realizados para os respectivos alcenos no estado fundamental e para as estruturas do estado de transição...

This paper proposes an integrated General Regression Neural Network (GRNN) adaptation scheme for dynamic plant modelling. The scheme can be used in a noisy and dynamic environment for online process control. It possesses several distinguished features compared to the original GRNN proposed by Specht, such as flexible pattern nodes add-in and delete-off mechanism, dynamic initial sigma assignmen...

STUDY OBJECTIVES Clinical prediction models for the diagnosis of obstructive sleep apnea (OSA) have lacked the accuracy necessary to confidently replace polysomnography (PSG). Artificial neural networks are computer programs that can be trained to predict outcomes based on experience. This study was conducted to test the hypothesis that a generalized regression neural network (GRNN) could accur...

Neural networks are a popular technology that exploits massive parallelism and distributed storage and processing for speed and error tolerance. Most neural networks tend to rely on linear, step or sigmoidal activation functions for decision making. The generalised regression neural network (GRNN) is a radial basis network (RBN) which uses the Gaussian activation function in its processing elem...

A new approach based on a Generalized Regression Neural Network (GRNN) has been proposed to predict the planform surface pressure field on a wing-tail combination in low subsonic flow. Extensive wind tunnel results were used for training the network and verification of the values predicted by this approach. GRNN has been trained by the aforementioned experimental data and subsequently was used ...

Short-term load forecasting plays a significant role in the management of power plants. In this paper, we propose multivariate adaptive step fruit fly optimization algorithm (MAFOA) to optimize smoothing parameter generalized regression neural network (GRNN) short-term forecasting. addition, due substantial impact some external factors including temperature, weather types, and date types on loa...

To improve prediction accuracy of top-coal drawing capability in steep seams, principal component analysis (PCA) and the general regression neural network (GRNN) are combined (PCA–GRNN model) to predict seams. Nine commonly used influencing factors selected establish a predictive index system for The PCA is eliminate correlation reduce dimensions various indices, thus obtaining three linearly u...

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical ...