Implementation of a fuzzy inference system using a normalized fuzzy neural network

1995 ◽  
Vol 75 (1) ◽  
pp. 17-31 ◽  
Author(s):  
Chun-Tang Chao ◽  
Ching-Cheng Teng
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy Neural

CHOQUET INTEGRAL–OWA BASED ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM WITH APPLICATION

2011 ◽  
Vol 10 (01) ◽  
pp. 15-34 ◽  
Author(s):  
YUANYUAN CHAI ◽  
LIMIN JIA
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Choquet Integral ◽  
Fuzzy Rule ◽  
Anfis Model ◽  
Inference System ◽  
Fuzzy Neural ◽  
Human Thinking ◽  
Neural Fuzzy

In order to solve the defects of consequent part expression in ANFIS model and several shortcomings in FIS, this paper presents a Choquet Integral–OWA based Fuzzy Inference System, known as AggFIS. This model has advantages in consequent part of fuzzy rule, universal expression of fuzzy inference operator and importance factor of each criteria and each rule, which is trying to establish fuzzy inference system that can fully reflect the essence of fuzzy logic and human thinking pattern. If we combine AggFIS with a feed forward-type neural network according to the basic principles of fuzzy neural network, we can obtain Choquet Integral–OWA based Adaptive Neural Fuzzy Inference System, which is named Agg-ANFIS. We apply this Agg-ANFIS model into the evaluation of traffic level of service. The experimental results show that Choquet Integral–OWA based Adaptive Neural Fuzzy Inference System (Agg-ANFIS) is a universal approximator because of its infinite approximating capability by training and can be used in complex systems modeling, analysis and prediction.

Model Identification of 3R Palnar Robot using Neural Network and Adaptive Neuro-Fuzzy Inference System

2021 ◽  
Author(s):  
R. Subasri ◽  
R. Meenakumari ◽  
R. Velnath ◽  
Srinivethaa Pongiannan ◽  
M. Sri Sai Mani Rohit Kumar
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Model Identification ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals AN ALGORITHM OF FUZZY INFERENCE SYSTEM FOR ISAR IMAGE CLASSIFICATION

2017 ◽  
Vol 2 ◽  
pp. 154
Author(s):  
Krasimir Slavyanov ◽  
Chavdar Minchev
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Information Analysis ◽  
Simulation Experiments ◽  
Inference System ◽  
Classification Procedure ◽  
Neural Network Simulation ◽  
Mamdani Fuzzy Inference System

This article offers an original ISAR image classification procedure based on Mamdani fuzzy inference system (FIS) dedicated to compute multiple results each from different type of analyzing criteria. The modeling and information analysis of the FIS are developed to draw a general conclusion from several results each produced by classification from neural network. Simulation experiments are carried out in MATLAB environment.

scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

scholarly journals Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

2015 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Panchand Jha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Inverse Kinematics ◽  
Fuzzy Inference ◽  
Robot Manipulator ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural ◽  
Neural Fuzzy

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.</span>

Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system

2009 ◽  
Vol 9 (2) ◽  
pp. 746-755 ◽  
Author(s):  
Mohammad Zounemat-Kermani ◽  
Ali-Asghar Beheshti ◽  
Behzad Ataie-Ashtiani ◽  
Saeed-Reza Sabbagh-Yazdi
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Scour Depth ◽  
Pile Groups ◽  
Inference System ◽  
Neuro Fuzzy
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