An intelligent backstepping sliding-mode control system using radial basis function network (RBFN) for a two-axis motion control system using permanent magnet linear synchronous motors (PMLSMs) is proposed. First, single-axis motion dynamics with the introduction of a lumped uncertainty, including cross-coupled interference between the two-axis mechanism, is derived. Then, to improve the contro...

energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. however, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. accordingly, in this paper a new strategy is proposed for electricity price forecast. the forecast strategy includes wavelet transform (wt...

In this paper, a hybrid network based on the combination of Radial Basis Function Networks (RBFNs) and Gaussian Mixture Models (GMMs) is proposed and used for speaker recognition. The hybrid network is a hierarchical one, where a GMM is built for each speaker and an RBFN is built for each group of speakers. The GMMs and RBFNs are trained independently. The RBFNs are used as a rst stage coarse c...

Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in t...

The paper examines applicability of Hopfield Model (HFM) for weather forecasting in southern Saskatchewan, Canada. The model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN) and radial basis function network (RBFN). The data of temperature, wind speed and relative humidity were used to train and test the four models. With each model, ...

Emotion recognition, or computers' ability to interpret people's emotional states, is a rapidly expanding topic with many life-improving applications. However, most image-based emotion recognition algorithms have flaws since people can disguise their emotions by changing facial expressions. As result, brain signals are being used detect human increased precision. proposed systems could do bette...

Thermography is a passive and non-contact imaging technique used extensively in the medical arena, but in relation to breast care, it has not been accepted as being on a par with mammography. This paper proposes the analysis of thermograms with the use of artificial neural networks (ANN) and bio-statistical methods, including regression and receiver operating characteristics (ROC). It is desire...

Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. Accordingly, in this paper a new strategy is proposed for electricity price forecast. The forecast strategy includes Wavelet Transform (WT...

Function approximation is an important type of supervised machine learning techniques, which aims to create a model for an unknown function to find a relationship between input and output data. The aim of the proposed approach is to develop and evaluate a function approximation models using Radial Basis Function Neural Networks (RBFN) and Particles Swarm Optimization (PSO) algorithm. We propose...