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 main target of this research work is to model the output performance adsorption water desalination system (AWDS) in terms switching and cycle time using artificial intelligence. ADC expressed by specific daily production (SDWP), coefficient (COP), cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) SDWP, COP, SCP was built measured data. To demonstrate superi...

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 ...

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...

In this paper river stage discharge models using Adaptive NeuroFuzzy Inference System (ANFIS) and Linear Multiple Regression (MLR) methods have been developed. This paper also investigates the best model to forecast river discharge. From the literature it is clear that ANN models and Fuzzy logic models are quite applicable on river stage discharge modelling. Hence this present study carried out...

Determining the rules that lead to expansion of urban areas has always been a challenging factor in modeling. To overcome this issue, an ANFIS model is proposed enhance simulation growth through automatic production transition rules. Hence, 22 models were trained using different division methods and Cellular Automata-based Markov Chain (CA-MC) was developed examine their efficiencies. The resul...

In the current study two methods are evaluated for predicting the compressive strength of concrete containing metakaolin. Adaptive neuro-fuzzy inference system (ANFIS) model and stepwise regression (SR) model are developed as a reliable modeling method for simulating and predicting the compressive strength of concrete containing metakaolin at the different ages. The required data in training an...

In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated ...

Optical character recognition (OCR) is becoming a powerful tool in the field of Character Recognition, now a days. In the existing globalized environment, OCR can play a vital role in different application fields. Basically, OCR technique converts images into editable format. This technique converts images in the form of documents such as we can edit, modify and store data more safely for long ...

This paper presents the design of the hybrid PID-ANFIS controller for controlling the speed of the Brushless Direct Current Motor (BLDCM). The objective of this work is to obtain the BLDCM speed controller equipment effectively. The hybrid PID-ANFIS controller is a combination of classical PID controller and ANFIS controller. This method is applied to BLDCM speed controller by simulation and im...