Amir Mohammadian

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Received Jul 27, 2014 Revised Sep 16, 2014 Accepted Sep 30, 2014 This paper presents a hybrid approach involving signal to noise ratio (SNR) and particle swarm optimization (PSO) for design the optimal and intelligent proportional-integral-derivative (PID) controller of an automatic voltage regulator (AVR) system with uses an adaptive neuro fuzzy inference(More)
The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature(More)
This paper presents an intelligent Proportional Integral Derivative (PID) controller for Automatic Voltage Regulator (AVR) system using Adaptive Neuro Fuzzy Inference System (ANFIS). In the proposed method, the PID controller parameters are tuned off line by using combination of Signal to Noise Ratio (SNR) and Particle Swarm Optimization (PSO) algorithm to(More)
The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature(More)
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