A new method for computing the resonant frequency of the circular microstrip antenna, based on the adaptive neuro-fuzzy inference system (ANFIS), is presented. A hybrid learning algorithm is used to identify the parameters of ANFIS. The results of the new method are in excellent agreement with the experimental results reported elsewhere.

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 ...

This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system. Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a train...

Conventional control algorithms used in pH control systems give inefficient performance, leading to use of large mixers. To improve the neutralization control process, an ANFIS based advanced controller has been proposed. In this paper, method of design of adaptive controller based on neurofuzzy technique is presented. The method uses ANFIS methodology to 1 / 4

Nonlinear Adaptive Noise Cancellation for 2-D Signals with Adaptive Neuro-Fuzzy Inference Systems Hao Qin Advisor: University of Guelph, 2004 Professor Simon X. Yang Neuro-fuzzy systems are capable of inducing rules from observations, where the adaptive neuro-fuzzy inference system (ANFIS) is an effective method that can be applied to a variety of domains such as pattern recognition, robotics, ...

This paper presents a PID tuning method for unstable processes using an adaptive-network-based-fuzzy-inference system (ANFIS) for given gain and phase margin (GPM) speci)cations. PID tuning methods are widely used to control stable processes. However, PID controller for unstable processes is less common. In this paper, the PID controller parameters can be determined by the ANFIS. Because the de...

No Runge-Kutta method can be energy preserving for all Hamiltonian systems. But for problems in which the Hamiltonian is a polynomial, the Averaged Vector Field (AVF) method can be interpreted as a Runge-Kutta method whose weights bi and abscissae ci represent a quadrature rule of degree at least that of the Hamiltonian. We prove that when the number of stages is minimal, the Runge-Kutta scheme...

In this paper a fully explicit, stabilized projection method called the Runge-Kutta-Chebyshev (RKC) Projection method is presented for the solution of incompressible Navier-Stokes systems. This method preserves the extended stability property of the RKC method for solving ODEs, and it requires only one projection per step. An additional projection on the time derivative of the velocity is perfo...

This paper presents an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed on the basis of opti...

In this paper performance of Puma 560 manipulator is being compared for hybrid gradient descent and least square method learning based ANFIS controller with hybrid Genetic Algorithm and Generalized Pattern Search tuned radial basis function based Neuro-Fuzzy controller. ANFIS which is based on Takagi Sugeno type Fuzzy controller needs prior knowledge of rule base while in radial basis function ...