This paper presents a new approach for classifying transient phenomena in power transformers, which may be implemented in digital relaying for transformer differential protection. Discrimination of various operating conditions of the power transformer is achieved by combining wavelet transform with Adaptive Neuro Fuzzy Inference system (ANFIS). The wavelet transform is applied for the analysis ...

This study aims to predict daily carbon monoxide (CO) concentration in the atmosphere of Tehran by means of developed artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Forward selection (FS) and Gamma test (GT) methods are used for selecting input variables and developing hybrid models with ANN and ANFIS. From 12 input candidates, 7 and 9 variables are se...

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were health...

In this paper a pose invariant face recognition using neuro-fuzzy approach is proposed. Here adaptive neuro fuzzy interface system (ANFIS) classifier is used as neuro-fuzzy approach for pose invariant face recognition. In the proposed approach the preprocessing of image is done by using adaptive median filter. It removes the salt pepper noise from the original images. From these denoised images...

In power systems, the constant frequency, voltage, and output are desired determine quality of generated electrical energy. Therefore, frequency control is crucial in systems. The parameters conventional controllers used generation plants determined according to system's characteristics at stage installation, they cannot adapt changing system dynamics as lifespan increases. Thus, studies on aut...

BACKGROUND Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. METHODS The AI system can automatically accomplish the opti...

Biological and physical treatment in wastewater plants appears to be one of the most important variables water quality management planning. This crucial characteristic, on other hand, is difficult quantify takes a long time obtain precise results. Scientists have sought devise several solutions address these issues. Artificial intelligence models are technique monitor pollutant parameters more ...

Fuzzy systems (FSs) are popular and interpretable machine learning methods, represented by the adaptive neuro-fuzzy inference system (ANFIS). However, they have difficulty dealing with high-dimensional data due to curse of dimensionality. To effectively handle ensure optimal performance, this paper presents a deep neural fuzzy (DNFS) based on subtractive clustering-based ANFIS (SC-ANFIS). Inspi...

Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to explore the dynamics of neural networks in forecasting crop (tomato) yield using environmental variables; here we aim at giving accurate yield amount. We use the Adaptive Neuro-Fuzzy Inference System (ANFIS). The input to ANFIS is several parameters de...

The conventional Proportional-Integral (PI) speed control has been widely used in industrial motor controls due to its capabilities in controlling linear plants. However, motor behaves as non-linear plant where the PI speed control may not be able to provide precise speed responses. With the fast growing of artificial intelligent in motor controls, the fuzzy logic and Adaptive Network Fuzzy Inf...