On a granular functional link network for classification

In this paper, we present a new granular classifier in two versions (iterative and non–iterative), by adopting some ideas originating from a kind of Functional Link Artificial Neural Network and the Functional Network schemes. These two architectures are substantially the same: they both use a function basis instead of the usual activation function, but they are different for the learning algorithm. We augment them from the perspective of Granular Computing and information granules, designing a new kind of classifier and two learning algorithms, by taking into account granularity of information. The proposed classifier exhibits the advantages of the granular architectures, that is higher accuracy and transparency. We formally discuss the convergence of the iterative learning scheme. We carry out some numerical experiments using publicly available data, by comparing the results against those results produced by the state-of-the-art methods. In particular, we achieved sound results by invoking the iterative learning scheme.