Abstract Radial Basis Function neural networks (RBFNNs) represent an attractive alternative
to other neural network models. One reason is that they form a unifying link between function …

Radar emitter classification (REC) is very important in both civil and military fields. It
becomes more and more difficult to classify the intercepted radar signals with the increasing …

Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to
establish an accurate prediction model using traditional model-driven methods. To address …

The development of cognitive radio and radar electronic reconnaissance has put forward an
important demand for improving the recognition ability of modulated signals in complex …

This paper proposes the multi-kernel neural networks and applies them to model the
nonlinear unsteady aerodynamics at constant or varying flow conditions. Different from …

The development of integrated avionics systems and electromagnetic spectrum technology
has attracted widespread attention. It has further increased the performance requirements …

Abstract Radial Basis Function Neural Networks (RBFNNs) have been widely used for
classification and function approximation tasks. Hence, it is worthy to try improving and …

In this paper, a self-organizing fuzzy neural network with adaptive gradient algorithm
(SOFNN-AGA) is proposed for nonlinear systems modeling. First, a potentiality of fuzzy rules …

Abstract Named Data Networking (NDN) is a promising network architecture being
considered as a possible replacement for the current IP-based (host-centric) Internet …

The purpose of this paper is to investigate a new method for EEG signals classification. A
powerful method for detecting these signals can greatly contribute to areas such as making …