GRNNGeneralized Regression Neural Network
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Results show that the consensus using GRNN mostly outperforms all the models included in the study and the consensus models, including the ensemble mean, IVCN, and OFCI (the official, interpolated NHC forecast).
The parameter s of GRNN model (also called 'spread' in MATLAB) determines the generalization capability of the GRNN.
Wang, "Prediction of mine gas content based on generalized regression neural network GRNN," China Coalbed Methane, vol.
In addition, all the techniques, including the conventional ANN, GRNN, and SVM methods, and the relative novel methods (ELM, ANFIS, and GMDH) proposed by this study remarkably differ in terms of their structures, principles, and parameters.
For the four different preprocessing methods (no preprocessing, MSC, SG, and MSC-SG), PNN and GRNN prediction model was established as follows: (1) the input layers had 5, 21, 17, and 21 nodes, respectively, representing the texture (CON, ENE, COR, and IDM) and colour (AGL) image features of CWs; (2) the nodes of hidden layer were determined by network adaptation; (3) the output layer represented the adulteration ratio; (4) the expansion constant was determined by experiments, where PNN was intended to be 0.3, 0.1, 0.2, and 0.1, and GRNN was intended to be 0.2, 0.1, 0.3, and 0.1.
GRNN has only one (smoothness) parameter, and its convergence is guaranteed; fast and stable [36].
GRNN computes the most probable value of an output y given only training vectors x.
The study of driving risk detection is carried out based on GRNN model in deceleration zone of expressway, which aims to reduce the incidence of accidents and provide security for drivers' safety.
In the case of the artificial neural networks, we used the Generalized Regression Neural Network (GRNN), the architecture of which is shown in Figure 4, and the data of [mathematical expression not reproducible], and [T.sub.U] (UAV position, UAV relay position, and UAV traffic to UAV relay, respectively).
Then, we perform numerical simulations to make a fair assessment for the GPR method, and the results are compared with the MNR method, the generalized regression neural network (GRNN) method, and the TC methods [17].
In practical applications, there is a large number of neural networks with modified algorithms, such as ELM [43-45], back-propagation neural network (BPNN) [46-48], and general regression neural network (GRNN) [49-51].
Kulkarni, "Modeling and monitoring of batch processes using principal component analysis (PCA) assisted generalized regression neural networks (GRNN)," Biochemical Engineering Journal, vol.