In order to further improve the precision and generalization ability of the neural network based performance model of engine, back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) have been investigated. The topologies and algorithms of these three different types of neural networks have been designed to meet the sa...

Freshwater reservoirs are considered as the source of atmospheric greenhouse gas (GHG), but more than 96% of global reservoirs have never been monitored. Compared to the difficulty and high cost of field measurements, statistical models are a better choice to simulate the carbon emissions from reservoirs. In this study, two types of Artificial Neural Networks (ANNs), Back Propagation Neural Net...

In a water distribution system (WDS), chlorine disinfection is important in preventing the spread of waterborne diseases. By strictly controlling residual chlorine throughout the WDS, water quality managers can ensure the satisfaction and safety of their customers. However, due to the travel time of water between the chlorine dosing point and any strategic monitoring points, water treatment pla...

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...