The design methodology of radial basis function neural networks based on fuzzy K-nearest neighbors approach

Various approaches to partitioning of high-dimensional input space have been studied with the intent of developing homogeneous clusters formed over input and output spaces of variables encountered in system modeling. In this study, we propose a new design methodology of a fuzzy model where the input space is partitioned by making use of some classification algorithm, especially, fuzzy K-nearest neighbors (K-NN) classifier being guided by some auxiliary information granules formed in the output space. This classifier being regarded in the context of this design as a supervision mechanism is used to capture the distribution of data over the output space. This type of supervision is beneficial when developing the structure in the input space. It is demonstrated that data involved in a partition constructed by the fuzzy K-NN method realized in the input space show a high level of homogeneity with regard to the data present in the output space. This enhances the performance of the fuzzy rule-based model whose premises in the rules involve partitions formed by the fuzzy K-NN. The design is illustrated with the aid of numeric examples that also provide a detailed insight into the performance of the fuzzy models and quantify several crucial design issues.