Abstract Traditional atmospheric models are based on the analysis and fitting of various factors influencing space atmosphere density. Neural network do not specifically analyze polynomials each factor in model, but use large data sets for construction. Two traditional model algorithms analyzed, main affecting identified, an neural networks containing is proposed. According to simulation error,...

The inverse problem in acousto-electric tomography concerns the reconstruction of electric conductivity a body from knowledge power density function interior body. This results currents prescribed at boundary electrodes, and it can be obtained through electro-static measurements together with auxiliary acoustic probing. Previous works on used continuum model for electrostatic conditions; howeve...

Abstract— Electromyography (EMG) signal provides a significant source of information for identification of neuromuscular disorders. This paper presents an application of neural network classifier on classification and identification of different normal and auto aggressive actions of hands and legs. Eight features that are extracted from eight channel EMG signals representing these actions have ...

Abstract: Our purpose of this paper is to solve a class of stochastic linear complementarity problems (SLCP) with finitely many elements. Based on a new stochastic linear complementarity problem function, a new semi-smooth least squares reformulation of the stochastic linear complementarity problem is introduced. For solving the semi-smooth least squares reformulation, we propose a feasible non...

In this paper, a Levenberg-Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which ...