A cluster based approach to reduce pattern layer size for generalized regression neural network

Generalized regression neural network (grnn), is a radial basis function based supervised learning type artificial neural network (ann) which is commonly used for data predictions. in addition to its easy modelling structure, being fast and producing accurate results are the other strong features of it. on the other hand, grnn employs a neuron in pattern layer for each data sample in training data set. therefore, for huge data sets pattern layer size increases proportional to the number of samples in training data set, memory requirement and computational time also increase excessively. in this study, in order to reduce space and time complexity of grnn, k-means clustering algorithm which had been used as pre-processor in the literature is utilized and outlier data emergence which affects the performances of previous studies negatively, is prevented by identifying test data located between clusters. hence, while memory requirement in pattern layer and number of calculations are reduced, negative effect on the performance emerged by the use of clustering algorithm is significantly removed and almost the same prediction performances to that of standard grnn are achieved by using 90% less training samples.