This paper is an investigation on negative emotions states recognition by employing of Fuzzy Adaptive Resonance Theory (Fuzzy-ART) considering the changes in activities of autonomic nervous system (ANS). Specific psychological experiments were designed to induce appropriate physiological responses on individuals in order to acquire a suitable database for training, validating and testing the proposed procedure. In this research, the three physiological applied signals are Galvanic Skin Response (GSR), Heart Rate (HR) and Respiration Rate (RR). The first experiment which is named Shock was designed to determine a criterion for the change of physiological signals of each individual. In the second one, a combination of two sets of questions has been asked from the subjects to induce their emotions. Finally, Physiological responses were analyzed by Fuzzy-ART to recognize which question excites the negative emotions. Detecting negative emotions from neutral is obtained with total accuracy of 94%
This paper deals with the basic concepts, mathematical parameters and design aspects of the Neuro-Fuzzy logic based controller for Non-Linear process plant to control flow rate of a system. Many techniques such as conventional PID and also fuzzy controllers have been proposed to control the flow rate. In this paper a new control approach is composed which is called PID ANFIS controller. Finally, the operation features of the three methods have been compared in terms of system overall performance. The designing of the scheme and the corresponding results have been tested by employing Matlab-Simulink. It will be shown that the composed ANFIS based controller is more versatile in comparison with those two others, for Non-Linear process plants. In addition, there is a considerable comparison between the three controllers in the presence of disturbance and environmental noise.
This paper describes the Neuro-Fuzzy logic based controller design for the buck DC-DC converters. As it is obvious form the previous type of controllers (conventional PID) for such converters which have been designed under the worst case condition for high load and lowest line condition, they present a lower loop in band width, and the system response is sluggish. In this regard, since the neural networks have powerful learning capabilities, optimization abilities, and adaptation, in this paper the design and analysis for a Neuro-Fuzzy PWM based PD controller is proposed. Lastly, In order to show that the Neuro logic based PD controller provides robust control for non-linear power electronics variable switching structure such as the dc-dc buck converter, MATLAB Simulink software is employed to confirm the corresponding results validity.
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