Generalized regression neural network (GRNN) is a variation to radial basis neural networks.
[1] GRNN can be used for regression, prediction, and classification.
GRNN can also be a good solution for online dynamical systems.
GRNN represents an improved technique in the neural networks based on the nonparametric regression.
The idea is that every training sample will represent a mean to a radial basis neuron.
is the squared euclidean distance between the training samples
GRNN has been implemented in many computer languages including MATLAB,[3] R- programming language, Python (programming language) and Node.js.
Neural networks (specifically Multi-layer Perceptron) can delineate non-linear patterns in data by combining with generalized linear models by considering distribution of outcomes (sightly different from original GRNN).
[4] Similar to RBFNN, GRNN has the following advantages: The main disadvantages of GRNN are: