It is well known that a correlation exist between the pattern of partial discharge (PD)
behavior and the insulation quality. Since different sources of partial discharge have their …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system; neural networks provide a model …

In this paper, we propose a survey concerning the state of the art of the graph matching
problem, conceived as the most important element in the definition of inductive inference …

With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart
homes and hybrid electric vehicles, the design of a suitable Energy Management System …

With the rapid development of digital information, the data volume generated by humans
and machines is growing exponentially. Along with this trend, machine learning algorithms …

This paper presents a new on-line algorithm for creating a self-organizing fuzzy neural
network (SOFNN) from sample patterns to implement a singleton or Takagi-Sugeno (TS) …

A fuzzy min–max neural network based on data core (DCFMN) is proposed for pattern
classification. A new membership function for classifying the neuron of DCFMN is defined in …

An enhanced fuzzy min-max (EFMM) network is proposed for pattern classification in this
paper. The aim is to overcome a number of limitations of the original fuzzy min-max (FMM) …

This paper proposes a fuzzy min-max neural network classifier with compensatory neurons
(FMCNs). FMCN uses hyperbox fuzzy sets to represent the pattern classes. It is a supervised …

This paper introduces a number of modifications to the learning algorithm of the self-
organizing fuzzy neural network (SOFNN) to improve computational efficiency. It is shown …