Quantum-Membership-Function-Based Adaptive Neural Fuzzy Inference System

2013 ◽  
pp. 227-233 ◽  
Author(s):  
Cheng-Hsiung Chiang
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neural Fuzzy

scholarly journals HANDWRITING IDENTIFICATION USING ANFIS

2014 ◽  
Vol 3 (2) ◽  
pp. 464-471
Author(s):  
T. Devi
Keyword(s):  
Neural Network ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Column Vector ◽  
Great Efficiency ◽  
Inference System ◽  
Handwritten Text ◽  
Triangular Membership Function ◽  
Neural Fuzzy

A new method for handwriting identification was presented.Individual characters was separated from a word choosed from a paragraph of handwritten text image which is given as input to the system. Then each of the separated characters are converted into column vectors of 625 values that are later fed into the adaptive neural fuzzy inference system(ANFIS), which was calculate membership function(MF) and normalized firing strength.In our paper we were used triangular membership function and compare with others MF.The networks has been designed with single layered neural network corresponding to a character from a-z, the outputs of all the column vector is fed into network the which has been developed using the concepts of correlation, with the help of this the overall network is optimized with the help of column vector thus providing us with recognized outputs with great efficiency.

scholarly journals Adaptive Neural Fuzzy Inference System and Automatic Clustering for Earthquake Prediction in Indonesia

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohammad Nur Shodiq ◽  
Dedy Hidayat Kusuma ◽  
Mirza Ghulam Rifqi ◽  
Ali Ridho Barakbah ◽  
Tri Harsono
Keyword(s):  
Earthquake Prediction ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
The United States ◽  
Earthquake Risk ◽  
Eurasian Plate ◽  
Inference System ◽  
Automatic Clustering ◽  
Neural Fuzzy

Earthquake is a type of natural disaster. The Indonesian archipelago located in the world's three mega plates; they are Australian plate, Eurasian plate, and Pacific plate. Therefore, it is possible for applied of earthquake risk of mitigation. One of them is to provide information about earthquake occurrences. This information used for spatiotemporal analysis of earthquakes. This paper presented Spatial Analysis of Magnitude Distribution for Earthquake Prediction using adaptive neural fuzzy inference system (ANFIS) based on automatic clustering in Indonesia. This system has three main sections: (1) Data preprocessing, (2) Automatic Clustering, (3) Adaptive Neural Fuzzy Inference System. For experimental study, earthquake data obtained Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) and the United States Geological Survey’s (USGS), the year 2010-2017 in the location of Indonesia. Automatic clustering process produces The optimal number of cluster, that is 7 clusters. Each cluster will be analyzed based on earthquake distribution. Its calculate the b value of earthquake to get the seven seismicity indicators. Then, implementation for ANFIS uses 100 training epochs, Number of membership function (MFs) is 2, MFs type input is gaussian membership function (gaussmf). The ANFIS result showed that the system can predict the non-occurrence of aftershocks with the average performance of 70%.

scholarly journals A NEURAL FUZZY INFERENCE BASED ADAPTIVE CONTROLLER FOR NONHOLONOMIC ROBOTS

2011 ◽  
pp. 56-65
Author(s):  
Ting Wang ◽  
Fabien Gautero ◽  
Christophe Sabourin ◽  
Kurosh Madani
Keyword(s):  
Control Strategy ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Controller ◽  
Reference Trajectory ◽  
Inference System ◽  
Nonholonomic Robots ◽  
Real Robot ◽  
Neuro Controller ◽  
Neural Fuzzy

In this paper, we propose a control strategy for a nonholonomic robot which is based on an Adaptive Neural Fuzzy Inference System. The neuro-controller makes it possible the robot track a desired reference trajectory. After a short reminder about Adaptive Neural Fuzzy Inference System, we describe the control strategy which is used on our virtual nonholonomic robot. And finally, we give the simulations’ results where the robot have to pass into a narrow path as well as the first validation results concerning the implementation of the proposed concepts on real robot.

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