In this paper discrete choice models, Logit and Probit are examined in order to predict the economic recession or expansion periods in USA. Additionally we propose an adaptive neuro-fuzzy inference system with triangular membership function. We examine the in-sample period 1947-2005 and we test the models in the out-of sample period 2006-2009. The forecasting results indicate that the Adaptive ...

Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties. Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image...

The purpose of this study is to model and optimize the detection of tar in cigarettes during the manufacturing process and show that low yield cigarettes contain similar levels of nicotine as compared to high yield cigarettes while B (Benzene), T(toluene) and X (xylene) (BTX) levels increase with increasing tar yields. A neuro-fuzzy system which comprises a fuzzy inference structure is used to ...

the present article investigates the application of high order tsk (takagi sugeno kang) fuzzy systems in modeling photo voltaic (pv) cell characteristics. a method has been introduced for training second order tsk fuzzy systems using anfis (artificial neural fuzzy inference system) training method. it is clear that higher order tsk fuzzy systems are more precise approximators while they cover n...

suitable soil structure is important for crop growth. one of the main characteristics of soil structure is the size of soil aggregates. there are several ways of showing the stability of soil aggregates, among which the determination of the median weight diameter of soil aggregates is the most common method. in this paper, a method based on adaptive neuro fuzzy inference system (anfis) was used...

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...

This paper presents a comparative study of the performance of three versions of Adaptive Neuro-Fuzzy Inference System (ANFIS) hybrid model and two innovative hybrid models in the prediction of oil and gas reservoir properties. ANFIS is a hybrid learning algorithm that combines the rule-based inferencing of fuzzy logic and the back-propagation learning procedure of Artificial Neural Networks. Fu...

The neuro-fuzzy controller incorporates fuzzy logic algorithm with an artificial neural network (ANN) structure. The conventional PI controller is replaced by Adaptive NeuroFuzzy Inference System (ANFIS), which tunes the fuzzy inference system with hybrid learning algorithm, This makes fuzzy system training with performance of the neuro-fuzzy based vector controlled of the system under controll...

In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI)...

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...