The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of brain coming about from ionic current inside neurons brain. importance EEG signal mainly diagnosis different mental and neurodegenerative diseases abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep stroke, etc. very useful for someon...

infiltration rate is one of the most important soil physical parameters and is a basic input data in irrigation and drainage projects. although, a number of theoretical or experimental based equations are presented to describe this phenomenon but the evaluation of some new sciences such as artificial neural networks, for prediction of the phenomenon can be investigated. generally, the infiltrat...

the prediction of the joint angle position, especially during tremor bursts, can be useful for detecting, tracking, and forecasting tremors. thus, this research proposes a new model for predicting the wrist joint position during rhythmic bursts and inter‑burst intervals. since a tremor is an approximately rhythmic and roughly sinusoidal movement, neural oscillators have been selected to underli...

background: one issue of concern in water supply is the quality of water. measuring the qualitative parameters of water is time-consuming and costly. predicting these parameters using various models leads to a reduction in related expenses and the presentation of overall and comprehensive statistics for water resource management. methods: the present study used an artificial neural network (ann...

this article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (mpc) of a chemical plant. a combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (mimo) process with time delays.  an optimization procedure for a neural mpc algorithm based on this model is then developed. the p...

objective: social anxiety disorder (sad) is defined as a constant fear of being embarrassed or negatively evaluated in social situations or while doing activities in the presence of others. several studies have examined the role of certain variables that might predict response to treatment and may affect treatment outcome. the purpose of this study was to identify predictive variables of change...

In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-laye...