A control system for non identical dc-dc converters using Adaptive Neuro Fuzzy Inference System (ANFIS) is presented. The converters are connected in parallel and have a non identical inductance value, L1≠L2≠L3, with 10% tolerance. The objective of control system is to balance the output current of each converter. One of converters is used as reference. The current error, which is subtraction o...

A new concept regarding to the GPS/INS integration, based on artificial intelligence here is presented. Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, an INS/GPS inte...

Sudden water inrush has been a deadly killer in underground engineering for decades. Currently, especially in developing countries, frequent water inrush accidents still kill a large number of miners every year. In this study, an approach for predicting the probability of fault-induced water inrush in underground engineering using the adaptive neuro-fuzzy inference system (ANFIS) was developed....

A new speed control approach based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) to a closed-loop, variable speed induction motor (IM) drive is proposed in this paper. ANFIS provides a nonlinear modeling of motor drive system and the motor speed can accurately track the reference signal. ANFIS has the advantages of employing expert knowledge from the fuzzy inference system and the learni...

This paper describes the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) model with SVD for classification of Electrocardiogram(ECG) signals into one of the few known categories, and to arrive at a diagnostic decision regarding the condition of the patient. The proposed architecture is a combination of Singular Value Decomposition (SVD) filtering method and ANFIS model. The ECG sig...

This paper addresses about an approach that suggests for stock portfolio optimization using the combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Capital Asset Pricing Model (CAPM). Stock portfolio optimization aims to determine which of the stocks to be added to a portfolio based on the investor’s needs, changing economic and market conditions. In order to construct an efficient...

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) were used to estimate shear stress distribution in streams. The methods were applied to the 145 field data gauged from four different sites on the Sarimsakli and Sosun streams in Turkey. The accuracy of the applied models was compared with the multiple-linear regression (MLR). The results showed t...

Transformer fault diagnosis is an interesting subject for plant operators due to its criticality in power systems. There are several international standards available to interpret power transformer faults based on dissolved gas analysis. In certain cases these standards are not able to provide correct diagnosis. There are several soft computing techniques available for modelling transformer fau...

In this study, Gaussian white noise and color noise of speech signal are reduced by using adaptive filter and soft computing algorithms. Since the main target is noise reduction of speech signal in a car, ambient noise recorded in a BMW750i is used as color noise in the applications. Signal Noise Ratios (SNR) are selected as +5, 0 and -5 dB for white and color noise. Normalized Least Mean Squar...

Stock market prediction is forever important issue for investor. Computer science plays vital role to solve this problem. From the evolution of machine learning, people from this area are busy to solve this problem effectively. Many different techniques are used to build predicting system. This research describes different state of the art techniques used for stock forecasting and compare them ...