IC-FNN: A Novel Fuzzy Neural Network With Interpretable, Intuitive, and Correlated-Contours Fuzzy Rules for Function Approximation

2018 ◽  
Vol 26 (3) ◽  
pp. 1288-1302 ◽  
Author(s):  
Mohammad Mehdi Ebadzadeh ◽  
Armin Salimi-Badr
Keyword(s):  
Neural Network ◽  
Function Approximation ◽  
Fuzzy Neural Network ◽  
Fuzzy Rules ◽  
Fuzzy Neural

Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone

2020 ◽  
Vol 17 (6) ◽  
pp. 2755-2762
Author(s):  
Pramoda Patro ◽  
Krishna Kumar ◽  
G. Suresh Kumar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Problem ◽  
Fuzzy Neural Network ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Fuzzy Neural ◽  
Approximate Function ◽  
Age Prediction

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.

Face Recognition Method Based on the Fuzzy Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 134-137
Author(s):  
Xiang Pan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Fuzzy Neural Network ◽  
Experimental Results ◽  
Fuzzy Rules ◽  
Good Effect ◽  
Recognition Method ◽  
Fuzzy Neural ◽  
Artificial Neural

This paper discusses a face recognition method based on the fuzzy neural network (FNN). The fuzzy neural network has more advantages than artificial neural network alone. The paper firstly introduces the structure of the FNN. Than proposed the fuzzy rules and the study algorithm. Thirdly it researches on the process of face recognition. The experimental results prove that this method can achieve good location performance and good effect of extraction.

An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network

2005 ◽  
Vol 150 (2) ◽  
pp. 211-243 ◽  
Author(s):  
Gang Leng ◽  
Thomas Martin McGinnity ◽  
Girijesh Prasad
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Rules ◽  
Line Extraction ◽  
Fuzzy Neural ◽  
On Line

scholarly journals A New Construction Method of Fuzzy Neural Network and Automatic Generation of Fuzzy Rules

1996 ◽  
Vol 116 (8) ◽  
pp. 964-972
Author(s):  
Ichirou Ishimaru ◽  
Taiji Kitagawa ◽  
Toshio Asano ◽  
Tomoaki Sakata ◽  
Hiroyasu Sasaki ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Automatic Generation ◽  
Construction Method ◽  
Fuzzy Rules ◽  
Fuzzy Neural ◽  
New Construction

A FUZZY NEURAL NETWORK MODEL FOR FUZZY INFERENCE AND RULE TUNING

1994 ◽  
Vol 02 (03) ◽  
pp. 265-277 ◽  
Author(s):  
KEON-MYUNG LEE ◽  
DONG-HOON KWANG ◽  
HYUNG LEEK WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Target System ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Fine Tune

It is relatively easy to create rough fuzzy rules for a target system. However, it is time-consuming and difficult to fine-tune them for improving their behavior. Meanwhile, in the process of fuzzy inference the defuzzification operation takes most of the inferencing time. In this paper, we propose a fuzzy neural network model which makes it possible to tune fuzzy rules by employing neural networks and reduces the burden of defuzzification operation. In addition, to show the applicability of the proposed model we perform an experiment and present its result.

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