Fernandes, S. E.N.Setoue, K. K.F. [UNESP]Adeli, H.Papa, J. P. [UNESP]2022-04-282022-04-282016-08-11Bio-Inspired Computation and Applications in Image Processing, p. 25-45.http://hdl.handle.net/11449/220833Many approaches using neural networks have been studied in the past years. A number of architectures for different objectives are presented in the literature, including probabilistic neural networks (PNNs), which have shown good results in several applications. A simple and elegant solution related to PNNs is the enhanced probabilistic neural networks (EPNNs), whose idea is to consider only the samples that fall in a neighborhood of given a training sample to estimate its probability density function. In this work, we propose to fine-tune EPNN parameters by means of metaheuristic-driven optimization techniques, from the results evaluated in a number of public datasets.25-45engEnhanced probabilistic neural networksMetaheuristicNeural networksOptimizationPattern recognitionCapítulo de livro10.1016/B978-0-12-804536-7.00002-82-s2.0-85017446492