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

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.

Quick-Return Servomechanism With Adaptive Fuzzy Neural Network Control

2000 ◽  
Vol 123 (2) ◽  
pp. 253-264
Author(s):  
Rong-Fong Fung ◽  
Faa-Jeng Lin ◽  
Rong-Jong Wai
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Adaptive Controller ◽  
Network Control ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Control Gain ◽  
Fuzzy Neural ◽  
On Line ◽  
The Mathematical Model

The dynamic response of an adaptive fuzzy neural network (FNN) controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this study. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton’s principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the quick-return servomechanism. Moreover, since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the quick-return servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behavior of the proposed adaptive FNN control system are robust with regard to parametric variations and external disturbances.

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.

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