Designing Radial Basis Function Networks for Classification Using Differential Evolution

Abstract

The construction of a quality RBF network for a specific application can be a time-consuming process as the modeller must select both a suitable set of inputs and a suitable RBF network structure. Evolutionary methodologies offer the potential to automate all or part of these steps. This study illustrates how a hybrid RBFN-DE system can be constructed, and applies the system to a number of datasets. The utility of the resulting RBFNs on these classification problems is assessed and the results from the RFBN-DE hybrids are shown to be competitive against the best performance on these datasets using alternative classification methodologies.

A Novel Radial Basis Function Networks Locally Tuned with Differential Evolution for Classification

“…As the current work is also a contribution in this direction, hence it is worth to discuss some of the promising works in this direction; thereby it is easier to see where our work will stand. (Hora et al, 2006), has used DE in RBFN to evolve model inputs, centers, width, weights and a classification cut off point towards developing a classifier. The work by Liu et al (2005, p.881), concerns the application of DE in RBFNs which consists of local and global tuning with adaptive fuzzy control.…”

Modifications for the Differential Evolution Algorithm

“…Furthermore, differential evolution was also incorporated in a neural architecture search [41]. The DE method has been applied with success to neural network training [42][43][44], to the Traveling Salesman Problem [45,46], training of RBF neural networks [47][48][49], and optimization of the Lennard Jones potential [50,51]. The DE method has also been successfully combined with other techniques for machine learning applications, such as classification [52,53], feature selection [54,55], deep learning [56,57], etc.…”