This chapter presents the modeling and simulation of an adaptive neuro-fuzzy inference strategy (ANFIS) to control one of the most important parameters of the induction machine, viz., speed. IM’s are non-linear machines having a complex and time-varying dynamics. Some of the states are inaccessible during the operational stages and also many of the states are not available for measurements; hen...

The summer season of 2003 in Continental Portugal was by far the worst in what respects both to the number of forest fires and the amount of burnt area. In the present work, burnt area monitoring was performed using NOAA/AVHRR imagery by means of a neuro-fuzzy (ANFIS) algorithm that was applied on an operational basis to the surface of the country. Validation of obtained results was performed b...

Gas turbines are currently a popular power generation technology in countries with access to natural gas resources. However they are very complex systems the operation of which at peak performance is challenging. This paper proposes the use of a hybrid approach based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the control of the speed and the exhaust temperature of a gas turbine. Th...

Pattern search learning is known for simplicity and faster convergence. However, one of the downfalls of this learning is the premature convergence problem. In this paper, we show how we can avoid the possibility of being trapped in a local pit by the introduction of stochastic value. This improved pattern search is then applied on a recurrent type neuro-fuzzy network (ANFIS) to solve time seri...

In the presented study, by using 0, 10, 20, 30, 40, 50 and 60 gr sodium hexametaphospathe (NaPO3) suspensions were prepared for hydrometer tests. At the end of the tests, particle diameter of the soil were calculated for each hydrometer reading time (0, 1, 2, 5, 10, 15, 30, 60, 120 and 260 min.). By using ANFIS (Adaptive Neuro-Fuzzy Infirence System) a model was developed to predict of the part...

This note is to point out and correct an error in Sezer et al. (2011). İn the paper (Sezer et al. 2011), the authors mention “ANFIS model has not been used for landslide susceptibility mapping previously”. This statement must be corrected as “The ANFIS model has been applied in landslide susceptibility mapping previously by Pradhan, Sezer, Gokceoglu, and Buchroithner (2010) in a different ...

Conductive silicone rubber has great advantages for tactile sensing applications. The electrical behavior of the elastomeric material is rate-dependent and exhibit hysteresis upon cyclic loading. Several constitutive models were developed for mechanical simulation of this material upon loading and unloading. One of the successful approaches to model the time-dependent behavior of elastomers is ...

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...

The aims of this research are to study application of high power ultrasound in preparation of stable oil-in-water emulsion. The effect of pH, ionic strength, pectin, Guar gum, lecithin, egg yolk, and xanthan gum as well as the time of sonication, temperature and viscosity of oil-water mixture on the specific surface area and size of droplets, and creaming index of the emulsion samples was inves...

The prediction of elastic modulus is one of the fundamental facts of structural engineering studies. The performance of adaptive neuro-fuzzy inference system (ANFIS) for predicting the elastic modulus of normaland high-strength concrete was investigated. Results indicate that the proposed ANFIS modeling approach outperforms the other given models in terms of prediction capability. According to ...