Inspired by the idea of basing the fuzzy logic inference procedure on a feedforward network structure, Jang [18] proposed a fuzzy neural network model - the Adaptive

In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based Computed Torque (PD) controller is applied to the dynamic model of puma 600 robot arm presented.. To validate

Accordingly, three artificial intelligence (AI) techniques comprising adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and adaptive

An adaptive neuro-fuzzy inference system (ANFIS) based intelligent control [25- 26] for vector controlled induction motor drive includes fuzzy logic algorithm with

A neural network based sliding mode adaptive control (NNSMAC) which is a combination of sliding mode technique, neural network approximation and adaptive technique is designed

The chapter is structured as follows, Section 2 Overview of SCARA robot control system, Section 3 describes the proposed Adaptive Neuro Fuzzy Inference System and Section

The neuro fuzzy classifier is used for classification is the Adaptive Network based Fuzzy Inference system(ANFIS).The developed neuro fuzzy classifier is tested

The neuro fuzzy classifier is used for classification is the Adaptive Network based Fuzzy Inference system(ANFIS).The developed neuro fuzzy classifier is tested for

Therefore, Adaptive Neuro Fuzzy Inference System (ANFIS) as neuro-fuzzy classifier is applied to churn prediction modeling and benchmarked to traditional rule- based classifier

This thesis presents the design and analysis of Neuro Fuzzy controller based on Adaptive Neuro-Fuzzy inference system (ANFIS) architecture for Load frequency

The DSTATCOM control scheme is based on adaptive neuro-fuzzy inference system (ANFIS) integrated with LMS based synchronous detection method inst.. The dc side

Adaptive Neuro Fuzzy Inference System (ANFIS) is a fuzzy mapping algorithm that is based on Tagaki-Sugeno-Kang (TSK) fuzzy inference system (Jang et al., 1997

The control law for proposed controller is as (4) form. K gain of the corrective control u c is adjusted with fuzzy logic and is the equivalent control u eq is computed by

Neuro-Fuzzy, Rotary Furnace, Bio-fuel, Artificial Neural Network (ANN), Adaptive Network - based Fuzzy Inference System (ANFIS), Agile Manufacturing Systems

This paper, adaptive neuro-fuzzy inference system for okra yield prediction, describes the use of neuro -fuzzy inference system in the prediction of okra yield using

Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference.. Systems

The most common recent structures of the hybrid neuro-fuzzy systems are: The Adaptive-Network-based Fuzzy Inference System (ANFIS), and the Fuzzy Wavelet Neural

Neural Network inverse model control strategy, Adaptive Neuro Fuzzy Inference System (ANFIS) used in mimicking another controller, and PD- Fuzzy logic will be