A self learning rough fuzzy neural network classifier for mining temporal patterns

2012 ◽  
Author(s):  
R. Sethukkarasi ◽  
U. Keerthika ◽  
A. Kannan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Temporal Patterns ◽  
Neural Network Classifier ◽  
Fuzzy Neural ◽  
Self Learning

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.

The Newly AND-OR FNN Modeling and Application

2012 ◽  
Vol 433-440 ◽  
pp. 846-852
Author(s):  
Jiang Hua Sui ◽  
Qiang Ma
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Self Organization ◽  
Rule Base ◽  
The Novel ◽  
Fuzzy Neural ◽  
Ship Control ◽  
Self Learning ◽  
Better Than

The novel multilayer feed-forward AND-OR fuzzy neural network (AND-OR FNN) is proposed in this paper. The main feature is shown not only in reducing the input space by special inner structure of neurons, but also auto-extracting the rules by the structure self-organization and parameter self-learning. The equivalent is proved that the network structure and fuzzy inference. The whole structure of network is optimized by genetic algorithm to extract if-then rules. This designing approach is employed to modeling an AND-OR FNN controller for ship control. Simulated results demonstrate that the number of rule base is decreased remarkably and the performance is much better than ordinary fuzzy control, illustrate the approach is practicable, simple and effective.

Self-Learning Control of PMA-Actuated Knee-Joint Rehabilitation Training Device Based on Fuzzy Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 1645-1650
Author(s):  
Xiao Li ◽  
Xia Hong ◽  
Ting Guan
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Learning Control ◽  
Training Device ◽  
Rehabilitation Training ◽  
Fuzzy Neural ◽  
Self Learning

To solve the problem of the delay, nonlinearity and time-varying properties of PMA-actuated knee-joint rehabilitation training device, a self-learning control method based on fuzzy neural network is proposed in this paper. A self-learning controller was designed based on the combination of pid controller, feedforward controller, fuzzy neural network controller, and learning mechanism. It was applied to the isokinetic continuous passive motion control of the PMA-actuated knee-joint rehabilitation training device. The experiments proved that the self-learning controller has the properties of high control accuracy and unti-disturbance capability, comparing with pid controller. This control method provides the beneficial reference for improving the control performance of such system.

Diagnosis and Diagnostic Rule Extraction Using Fuzzy Neural Network

2001 ◽  
Author(s):  
C. James Li ◽  
Chong-suhk Lee ◽  
Sun’an Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Rule Extraction ◽  
Training Data ◽  
Data Sets ◽  
Neural Network Classifier ◽  
Fuzzy Neural

Abstract The goal of this study is to develop a reasoning device and a diagnostic rule extraction methodology based on fuzzy neural network. This paper describes a method to obtain a fuzzy neural network classifier from labeled training data sets and algorithms to extracted linguistic diagnostic rules from such a trained fuzzy neural network. Benchmark comparisons were performed using three data sets from three different fields of applications. The proposed methodology was shown to outperform all the existing methods that were compared.

The cluster assessment of facial attractiveness using fuzzy neural network classifier based on 3D Moiré features

2014 ◽  
Vol 47 (3) ◽  
pp. 1249-1260 ◽  
Author(s):  
Wen-Chung Chiang ◽  
Hsiu-Hsia Lin ◽  
Chiung-Shing Huang ◽  
Lun-Jou Lo ◽  
Shu-Yen Wan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Facial Attractiveness ◽  
Neural Network Classifier ◽  
Fuzzy Neural
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