Integration of neural network algorithms, nonlinear dynamics models, and fuzzy logic methods in prediction problems

2008 ◽  
Vol 47 (1) ◽  
pp. 72-77 ◽  
Author(s):  
P. A. Borisov ◽  
G. P. Vinogradov ◽  
N. A. Semenov
Keyword(s):  
Neural Network ◽  
Nonlinear Dynamics ◽  
Fuzzy Logic ◽  
Network Algorithms ◽  
Dynamics Models ◽  
Prediction Problems

A Combination of Fuzzy Logic and Neural Network Algorithms for Active Vibration Control

1996 ◽  
Vol 210 (3) ◽  
pp. 153-167 ◽  
Author(s):  
S-J Huang ◽  
R-J Lian
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Vibration Control ◽  
Vibration Amplitude ◽  
Mimo System ◽  
Active Vibration Control ◽  
Lumped Mass ◽  
Network Algorithms ◽  
Neural Network Controller ◽  
Active Vibration

The construction of a dynamic absorber incorporating active vibration control is described. The absorber is a 2 degree of freedom spring-lumped mass system sliding on a guide pillar, with two internal vibration disturbance sources. Both the main mass and the secondary absorber mass were acted on by direct current (d.c.) servo motors, respectively, to suppress the vibration amplitude. In this paper, a new control approach is proposed by combining fuzzy logic and neural network algorithms to control the multi-input/multi-output (MIMO) system. Firstly, the fuzzy logic controller was designed for controlling the main influence part of the MIMO system. Secondly, the coupling neural network controller was employed to take care of the coupling effect and refine the control performance of the MIMO system. The experimental results show that the control system effectively suppresses the vibration amplitude and with good position tracking accuracy.

Review of Improvement of Web Search Based on Web Log File

2012 ◽  
Vol 3 (2) ◽  
pp. 298-300 ◽  
Author(s):  
Soniya P. Chaudhari ◽  
Prof. Hitesh Gupta ◽  
S. J. Patil
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Web Search ◽  
Pattern Mining ◽  
Efficiency Improvement ◽  
Web Searching ◽  
Important Method ◽  
Web Log ◽  
Journal Paper ◽  
Log File

In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

scholarly journals Management of the Torch Structure with the New Methodological Approaches to Regulation Based on Neural Network Algorithms

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1909
Author(s):  
Konstantin Osintsev ◽  
Sergei Aliukov ◽  
Yuri Prikhodko
Keyword(s):  
Neural Network ◽  
Coal Dust ◽  
Combustion Process ◽  
Analytical Calculation ◽  
Continuous Distribution ◽  
Initial Section ◽  
Distribution Functions ◽  
Thermophysical Characteristics ◽  
Network Algorithms ◽  
Furnace Volume

A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture, the time of combustion of particles of different masses and the temperature at the end of the zone of intensive combustion. The temperature of the torch, the speed of flame propagation, and the degree of particle burnout must be controlled. The authors propose an algorithm for controlling the thermophysical properties of the torch based on neural network algorithms. The system collects data for a certain time, transmits the information to the server. The data is processed and a forecast is made using neural network algorithms regarding the combustion modes. This allows to increase the reliability and efficiency of the combustion process. The authors present experimental data and compare them with the data of the analytical calculation. In addition, data for certain modes are given, taking into account the system’s operation based on neural network algorithms.

Dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms

2020 ◽  
Vol 39 (4) ◽  
pp. 5521-5534
Author(s):  
Ying Liu ◽  
Zhongqi Fan ◽  
Hongliang Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Management ◽  
Coal Mine ◽  
Evaluation System ◽  
Statistical Evaluation ◽  
Specific Situation ◽  
Network Algorithms ◽  
Management Capability ◽  
Coal Enterprises

By establishing the evaluation system of emergency management capability for coal mine enterprises, we can identify the problems and shortcomings in coal mine emergency management, improve and improve its emergency management capability for coal mine emergencies. In this paper, the authors analyze the dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms. Neural networks can form any form of topological structure through neurons, so they can directly simulate fuzzy reasoning in structure, that is to say, the equivalent structure of neural networks and fuzzy systems can be formed. This paper constructs the index system based on accident causes, and verifies the scientific rationality of the system. On this basis, according to the specific situation of coal mine emergency management, we design the evaluation criteria of coal mine emergency management capability evaluation index. Because coal mine accidents have the characteristics of complexity, variability and sudden dynamic, it is necessary to adjust and improve the accidents dynamically at any time. The model combines qualitative and quantitative indicators, and can make an overall evaluation of coal mine emergency management capability. It has the characteristics of clear results and strong fitting of simulation results.

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