Prediction System of Burning Through Point (BTP) Based on Adaptive Pattern Clustering and Feature Map

2006 ◽  
Author(s):  
Wu-shan Cheng
Keyword(s):  
Prediction System ◽  
Pattern Clustering ◽  
Feature Map ◽  
Burning Through Point ◽  
Adaptive Pattern

scholarly journals Intelligent Control of the Complex Technology Process Based on Adaptive Pattern Clustering and Feature Map

2008 ◽  
Vol 2008 ◽  
pp. 1-9
Author(s):  
Wushan Cheng
Keyword(s):  
Fuzzy Neural Networks ◽  
Sintering Process ◽  
Process Data ◽  
Pattern Clustering ◽  
Feature Map ◽  
Technology Process ◽  
Fuzzy Neural ◽  
Complex Technology ◽  
Burning Through Point ◽  
Adaptive Pattern

A kind of fuzzy neural networks (FNNs) based on adaptive pattern clustering and feature map (APCFM) is proposed to improve the property of the large delay and time varying of the sintering process. By using the density clustering and learning vector quantization (LVQ), the sintering process is divided automatically into subclasses which have similar clustering center and labeled fitting number. Then these labeled subclass samples are taken into fuzzy neural network (FNN) to be trained; this network is used to solve the prediction problem of the burning through point (BTP). Using the 707 groups of actual training process data and the FNN to train APCFM algorithm, experiments prove that the system has stronger robustness and wide generality in clustering analysis and feature extraction.

WIS-ART: UNSUPERVISED CLUSTERING WITH RAM DISCRIMINATORS

1992 ◽  
Vol 03 (01) ◽  
pp. 57-63 ◽  
Author(s):  
Eamon P. Fulcher
Keyword(s):  
Random Access ◽  
Test Phase ◽  
Adaptive Resonance Theory ◽  
The Self ◽  
Hierarchical Level ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Pattern Clustering ◽  
Adaptive Pattern ◽  
System Operating

WIS-ART merges the self-organising properties of Adaptive Resonance Theory (ART) with the operation of WISARD, an adaptive pattern recognition machine which uses discriminators of conventional Random Access Memories (RAMs). The result is an unsupervised pattern clustering system operating at near real-time that implements the leader algorithm. ART’s clustering is highly dependent upon the value of a “vigilance” parameter, which is set prior to training. However, for WIS-ART hierarchical clustering is performed automatically by the partitioning of discriminators into “multi-vigilance modules”. Thus, clustering may be controlled during the test phase according to the degree of discrimination (hierarchical level) required. Methods for improving the clustering characteristics of WIS-ART whilst still retaining stability are discussed.

Application of the Boundary Element Method and Modal Analysis to Tire Acoustics Problems

1993 ◽  
Vol 21 (2) ◽  
pp. 66-90 ◽  
Author(s):  
Y. Nakajima ◽  
Y. Inoue ◽  
H. Ogawa
Keyword(s):  
Finite Element ◽  
Boundary Element Method ◽  
Boundary Element ◽  
Acoustic Radiation ◽  
Traffic Noise ◽  
Noise Prediction ◽  
Prediction System ◽  
Tire Noise ◽  
Acoustic Contribution ◽  
Element Method

Abstract Road traffic noise needs to be reduced, because traffic volume is increasing every year. The noise generated from a tire is becoming one of the dominant sources in the total traffic noise because the engine noise is constantly being reduced by the vehicle manufacturers. Although the acoustic intensity measurement technology has been enhanced by the recent developments in digital measurement techniques, repetitive measurements are necessary to find effective ways for noise control. Hence, a simulation method to predict generated noise is required to replace the time-consuming experiments. The boundary element method (BEM) is applied to predict the acoustic radiation caused by the vibration of a tire sidewall and a tire noise prediction system is developed. The BEM requires the geometry and the modal characteristics of a tire which are provided by an experiment or the finite element method (FEM). Since the finite element procedure is applied to the prediction of modal characteristics in a tire noise prediction system, the acoustic pressure can be predicted without any measurements. Furthermore, the acoustic contribution analysis obtained from the post-processing of the predicted results is very helpful to know where and how the design change affects the acoustic radiation. The predictability of this system is verified by measurements and the acoustic contribution analysis is applied to tire noise control.

Short-Term Flood Prediction System

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Konrad Nering
Keyword(s):  
Flash Floods ◽  
Prediction System ◽  
Short Term ◽  
Data Set ◽  
Flood Prediction ◽  
Full Operation

AbstractThis paper describes a fully functional short-term flood prediction system. Its effect has been tested on watershed of Lubieńka river in Małopolska. To use this system it must have a data set also described in this paper. A modification of the system to adopt for predicting flash floods was described. Full operation of the system is shown on example of real flood on Lubieńka river in June 2011.

Health Prediction System Using Data Mining

2018 ◽  
Vol 15 (2) ◽  
pp. 696-699
Author(s):  
Kalyani Shahaji Zodage ◽  
Puja Sarage ◽  
Trupti Sudrik ◽  
Rashmi Sonawane
Keyword(s):  
Data Mining ◽  
Prediction System ◽  
Using Data
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