This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS). The control structure of the purposed consists fuzzy logic to damp the low frequency oscillations of power system and neuro identifier to track the dynamic behavior of the plant. In practical for damping of disturbance in the power system, Automatic Voltage Controller (AVR) is used. To develop this controller a...

In this paper neuro-fuzzy technique is used for the first time in modeling eco-friendly furnace parameters to predict the melting rate of the molten metal required to produce homogenous and quality castings. The relationship between the process variables (input) viz. flame temperature, preheat air temperature, rotational speed of the furnace dome, percentage of excess air, melting time, fuel co...

This paper presents the research and development of a hybrid neuro-fuzzy model for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent neuro-fuzzy multiagent systems that use MultiAgent Reinforcement Learning...

Abstract. In this paper an algorithm for neuro-fuzzy identification of multivariable discrete-time nonlinear dynamical systems is proposed based on a decomposed form as a set of coupled multiple input and single output (MISO) Takagi-Sugeno (TS) neuro-fuzzy networks. An on-line scheme is formulated for modeling a nonlinear autoregressive with exogenous input (NARX) neuro-fuzzy structure from sam...

This paper presents an efficient hybrid neurofuzzy control scheme for synchronous generator. The scheme proves to be beneficial as the control scheme targets for the better control of Synchronous generator when used along with other sub systems. The system is stabilize after t=0.13. The control parameter the performances indices of IAE are 4.755 for fuzzy and 7.242 for hybrid neuro fuzzy and fo...

As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the ident...

Original scientific paper This paper proposes a Neuro-fuzzy system for quantitative assessment of the effects of intelligent transportation systems and technologies on road fatalities. The basic idea in developing Neuro-fuzzy system is the fact that intelligent transportation systems and technologies activate some safety mechanisms and in turn the activation of safety mechanisms will have a pos...

Article Info Abstract This paper presents a Hybrid Adaptive Neuro Fuzzy Control technique for speed control of BLDC motor drives. The proposed controller is an integration of adaptive neuro fuzzy, fuzzy PID and PD controllers. The objective is to utilize the best attribute of fuzzy PID and PD controllers, which exhibits a better response than the neuro fuzzy controllers. The error back propagat...

In this paper, the problem of minimum-time trajectory planning is studied for a three degrees-offreedom (3-DOF) planar manipulator using a hierarchical hybrid neuro-fuzzy system. A first preprocessing step involves two components. The first component is NeFIK (for Neuro-Fuzzy Inverse Kinematics), a neuro-fuzzy network designed to learn and solve the inverse kinematics problem. The second one is...

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy archi...