Self-learning fuzzy neural network and its application to fire auto-detecting in fire protection systems

2002 ◽  
Author(s):  
Chen Shuangye ◽  
Yi Jikai ◽  
Zhao Yingyan
Keyword(s):  
Neural Network ◽  
Fire Protection ◽  
Fuzzy Neural Network ◽  
Protection Systems ◽  
Fuzzy Neural ◽  
In Fire ◽  
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.

Design in Fire Prevention Based on Multi-Sensor and WSN

2015 ◽  
Vol 713-715 ◽  
pp. 2237-2240
Author(s):  
Jun Ying Sun ◽  
Shu Yi Qi
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fire Prevention ◽  
Fusion System ◽  
Prevention System ◽  
Fuzzy Neural ◽  
Fire Detector ◽  
In Fire ◽  
Fire Information

A Fire Prevention system is designed, which uses the composite fire detector, integrating four various sensors of smokescope, temperature, CO density and gas to sample the four different fire information around the field. The fusion system of fire information based on fuzzy neural network is built that is used to make fusion on the detected fire information and confirm the final case of fire.

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.

Study of the Engineering Vehicles of Automatic Transmission Shift Schedule Based on Fuzzy Neural Network

2012 ◽  
Vol 591-593 ◽  
pp. 1720-1723 ◽  
Author(s):  
Yong Jing Huang ◽  
Jin Yao ◽  
Jia Hua Han ◽  
Di Wu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Automatic Transmission ◽  
Learning Ability ◽  
Shift Schedule ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Set Up ◽  
Takagi Sugeno ◽  
Self Learning

By combining the powerful self-learning ability of the neural network and the characteristic that the fuzzy control is designed based on the strategical rules of knowledge and language, this paper put forward the strategy of engineering vehicles' automatic transmission shift. According to a large number of experimental data, as well as the drivers' experience and the experts' profession, this paper put forward the strategy of engineering vehicles’ automatic transmission shift. The neural network model is set up based on Takagi-Sugeno and the factual cases are used to train and exam by MATLB, the simulation result showed that this method is feasible and meet the shift requirement, as it can accelerate effectively the establishment of the rules and reduce the set up time. The shift schedule can reflect precisely the actual out put target gear and meet the shift requirement.

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