In this study, EEG signals were classified by using the average powers extracted by means of the rectangle approximation window based average power method from the power spectral densities of frequency sub-bands of the signals and two different artificial neural networks (ANNs) which are adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLPNN). In order to ...

The aim of this study was to employ iota-carrageenan (IC) and wheat protein (WP) as an emulsifier alternative to egg yolk in a model mayonnaise system. A solution of 0.1% IC and 4% WP was prepared and used as an emulsifier in five different mayonnaise formulas. All mayonnaise treatments were evaluated and compared based on emulsion stability and viscosity at 4, 23, and 40 C. In addition, an ada...

In this study, an adaptive neuro-fuzzy inference system is designed for output voltage and frequency control of a variable-speed wind power generation system. Variable-speed wind power generation systems (VSWPGS) provide the opportunity to capture more power than fixed speed turbines. On the other hand, the variable-speed wind turbine output can be variable voltage and variable frequency for fl...

introduction: epilepsy is a clinical syndrome in which seizures have a tendency to recur. sodium valproate is the most effective drug in the treatment of all types of generalized seizures. finding the optimal dosage (the lowest effective dose) of sodium valproate is a real challenge to all neurologists. in this study, a new approach based on adaptive neuro-fuzzy inference system (anfis) was pre...

This paper proposes an artificialintelligence-based solution to interface photovoltaic (PV) array with the three phase ac load and to deliver maximum power to the load. The maximum power delivery to the load is achieved by MPPT controller which employs adaptive neuro-fuzzy inference system (ANFIS). The proposed ANFIS-based MPPT offers an extremely fast dynamic response with great accuracy. The ...

This paper investigates the ability of genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) techniques for groundwater depth forecasting. Five different GP and ANFIS models comprising various combinations of water table depth values from two stations, Bondville and Perry, are developed to forecast one-, twoand three-day ahead water table depths. The root mean square errors...

This paper reviews the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for vector-controlled induction motor drives. While conventional schemes do not deal well with the highly nonlinear nature of motor control, fuzzy logic with its adjustability and neural networks with their adaptability have been shown to be excellent alternatives. ANFIS combines the advantages of fuzzy logic and neura...

Electrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this present paper, we develop an adaptive neuro-fuzzy inference system (ANFIS) model for groundwater electrical ...

The low frequency electromechanical oscillations have been observed in many power systems and have resulted in system separation on several occasions. The main objective of this paper is to damp out the power angle oscillations of a two-area power system using Power System Stabilizers (PSSs) and Static Synchronous Series Compensator (SSSC) controllers. In this paper, the damping performances of...

This study presents a new method for modeling an adaptive neuro-fuzzy inference system (ANFIS) based on vibration for predicting surface roughness in the CNC turning process. The input parameters of the model are insert nose radius, cutting speed, feed rate, depth of cut and vibration amplitude, which determine the output parameter of the surface roughness. A Gauss type membership function was ...