Sensor fault detection for uninterruptible power supply (UPS) control system using fast fuzzy-neural network and immune network

2002 ◽  
Author(s):  
S. Taniguchi ◽  
Y. Dote
Keyword(s):  
Neural Network ◽  
Control System ◽  
Power Supply ◽  
Fuzzy Neural Network ◽  
Immune Network ◽  
Sensor Fault ◽  
Uninterruptible Power Supply ◽  
Fuzzy Neural ◽  
Sensor Fault Detection ◽  
Uninterruptible Power

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Fuzzy neural network control algorithm for asymmetric building structure with active tuned mass damper

2020 ◽  
Vol 26 (21-22) ◽  
pp. 2037-2049
Author(s):  
Xiao Yan ◽  
Zhao-Dong Xu ◽  
Qing-Xuan Shi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Linear Quadratic Regulator ◽  
Tuned Mass Damper ◽  
Linear Quadratic ◽  
Mass Damper ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Damper Control

Asymmetric structures experience torsional effects when subjected to seismic excitation. The resulting rotation will further aggravate the damage of the structure. A mathematical model is developed to study the translation and rotation response of the structure during seismic excitation. The motion equations of the structures which cover the translation and rotation are obtained by the theoretical derivations and calculations. Through the simulated computation, the translation and rotation response of the structure with the uncontrolled system, the tuned mass damper control system, and active tuned mass damper control system using linear quadratic regulator algorithm are compared to verify the effectiveness of the proposed active control system. In addition, the linear quadratic regulator and fuzzy neural network algorithm are used to the active tuned mass damper control system as a contrast group to study the response of the structure with different active control method. It can be concluded that the structure response has a significant reduction by using active tuned mass damper control system. Furthermore, it can be also found that fuzzy neural network algorithm can replace the linear quadratic regulator algorithm in an active control system. Because fuzzy neural network algorithm can control the process on an uncertain mathematical model, it has more potential in practical applications than the linear quadratic regulator control method.

Design of Greenhouse Wireless Sensor Network Control System Based on Fuzzy Neural Network

2011 ◽  
Vol 464 ◽  
pp. 318-321
Author(s):  
Rong Biao Zhang ◽  
Li Hong Wang ◽  
Xian Lin Huang ◽  
Jing Jing Guo
Keyword(s):  
Neural Network ◽  
Control System ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Environmental Data ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Fuzzy Neural

This paper proposed a greenhouse control system utilizing wireless sensor network (WSN) to overcome the wiring difficulties and poor mobility in the application of traditional cable-used control systems. Each wireless sensor node in the WSN collects the environmental data of temperature, humidity and CO2 concentration, and transmits the data to the control center via the sink nodes. A fuzzy neural network with three inputs and six outputs was designed to improve the control accuracy. By analyzing the relationship between the mentioned environmental factors above and the actuators of the system, a fuzzy rule was made and combined with the neural network. The simulation results showed that the proposed method could respond in a short time with high accuracy, and had small overshoot as well as good stability.

The Intelligent Scheduling Method of Elevator Group Control Based on Fuzzy Neural Network

2012 ◽  
Vol 155-156 ◽  
pp. 653-657
Author(s):  
Yu Lin Dong ◽  
Xiao Ming Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Scheduling Algorithm ◽  
Mathematic Model ◽  
Group Control ◽  
Fuzzy Neural ◽  
And Performance ◽  
Elevator Group Control

Elevator group control system (EGCS) is a complex optimization system, which has the characteristics of multi-objective, uncertain, stochastic random decision-making and nonlinear. It is hard to describe the elevator group control system in exact mathematic model and to increase the capability of the system with traditional control method. In this paper, we aim at the characters of elevator group control system and intelligent control, introduce the system's control fashion and performance evaluate guidelines and propose an elevator group control scheduling algorithm based on fuzzy neural network.

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