Generalized regression neural networks GRNN

Some historically important artificial neural networks are Hopfield Networks, Per-ceptron Networks and Adaline Networks, while the most well-known are Backpropa-gation Artificial Neural Networks (BP-ANN), Kohonen Networks (K-ANN, or Self-Organizing Maps, SOM), Radial Basis Function Networks (RBFN), Probabilistic Neural Networks (PNN), Generalized Regression Neural Networks (GRNN), Learning Vector Quantization Networks (LVQ), and Adaptive Bidirectional Associative Memory (ABAM). 

General regression neural network (GRNN) was introduced by Donald Specht in 1991 [33], and it has been successfully used in pharmacokinetic studies, including human intestinal absorption [34], blood-brain barrier prediction [35], human serum albumin binding [35], milk-plasma ratio [35], and drug clearance [36], Recently it has been applied for the prediction of Tetrahymenapyriformis toxicity [37],... 

Initially, networks were trained from data obtained from the experimental design conditions given in Figure 7.3. These were radial basis function (RBF) networks, multilayer perception (MLP) networks, probabilistic neural networks (PNNs), and generalized regression neural networks (GRNNs), as well... 

Ren and Gao [27] developed a technique which combines data fusion (DF) and multiscale wavelet transformations with generalized regression neural network (GRNN) and applied it for analyzing overlapping spectra. The main role of the hybrid method, DF-GRNN, is to enhance... 

Gobburu, J. V. S., Shelver, W. H., and Chen, E. P. (1996) Initial assessment of generalized regression neural networks (GRNN) in QSAR. Abstr. Pap. Am. Chem. Soc. 212th, MEDI-004. 

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