Prediction of the Slagging Characteristics of Coal Ash Based on Symmetric Fuzzy Cross Entropy and Vague Sets

2013 ◽  
Vol 302 ◽  
pp. 617-621
Author(s):  
Xiao Qiang Wen
Keyword(s):  
Pattern Recognition ◽  
Prediction Model ◽  
Coal Ash ◽  
Softening Temperature ◽  
Cross Entropy ◽  
Accuracy Rate ◽  
Recognition Model ◽  
Vague Sets ◽  
Acid Ratio ◽  
Sets Theory

A prediction model was built to predict the slagging characteristics of coal ash based on the theory of symmetric fuzzy cross entropy and vague sets. Softening temperature, SiO2-Al2O3 ratio, Alkali-acid ratio, silicon percentage of value were selected as input vectors. Firstly, the vague values of the selected samples were calculated based on vague sets theory, and then the similarity degree was evaluated based on symmetric fuzzy cross entropy. The prediction results prove that the predicting accuracy rate of the new pattern recognition model is 90%. So, the model built here is reasonable and feasible and meets the requirement of engineering.

Prediction of the Slagging Characteristics of Coal Ash Based on Vague Sets

2013 ◽  
Vol 668 ◽  
pp. 954-958
Author(s):  
Xiao Qiang Wen ◽  
Ling Fang Sun
Keyword(s):  
Pattern Recognition ◽  
Prediction Model ◽  
Coal Ash ◽  
Distance Measures ◽  
Recognition Model ◽  
Vague Sets ◽  
Coal Ashes

The distance measures of similarity between vague sets (values) have been developed to solve the problem of slagging characteristic of coal ash, and a slag-prediction model of coal ash based on vague sets was proposed. In the model, four single indices and their bounds are involved. The indices are ST, SiO2/Al2O3, B/A and G. At the same time, 25 coal ashes were selected as samples. the model was employed to predict the slagging characteristic of the 10 coal ashes. Through predicting, determining and comparison, it proved that the accuracy of the pattern recognition model was 90 percent and met the engineering requirements. So the model built was reasonable and feasible.

Comparison and Application of Two Mathematical Models in Predicting and Determining the State of Deposition and Slagging of Coal Burning Boiler

2013 ◽  
Vol 818 ◽  
pp. 240-245
Author(s):  
Xiao Qiang Wen
Keyword(s):  
Power Plant ◽  
Softening Temperature ◽  
The State ◽  
Vague Sets ◽  
Coal Burning ◽  
Average Temperature ◽  
Acid Ratio ◽  
Normal Method ◽  
High Level ◽  
Temperature Furnace

Building a model to predict the state of slag on coal-fired boilers is a good way to optimize the coal combustion and reduce the risk of boiler slag. This paper built new models based on vague sets to predict the state of slag on coal-fired boilers, in which there were six input vectors, which were softening temperature, SiO2-Al2O3 ratio, alkali-acid ratio, percentage of silicon content, the dimensionless average temperature furnace and the dimensionless inscribed circle diameter furnace, and one output vectors, which was slagging degree. Two methods, which were based on the sense of distance and symmetric fuzzy cross entropy, were proposed to calculate the similarity between vague sets. 10 coal burning boilers were selected as known samples and the feasibility of the new methods was proved by the result of predicting the state of slag on the four coal burning boilers from Jilin heat and power plant, Xinli power plant, Jinzhou power plant and Qinhuangdao power plant. Through predicting and determining, it proves that the two pattern recognition models are high in prediction accuracy. Compared with the normal method, it is easier for operators to predict, determine the slagging state and reduce disturbance as far as possible. Besides, a prediction system has been developed by object-oriented high-level language accordingly.

The fusion mechanism of complex minerals mixture and prediction model for flow temperature of coal ash for gasification

Fuel ◽  
2021 ◽  
Vol 305 ◽  
pp. 121448
Author(s):  
Wenju Shi ◽  
Marcel Laabs ◽  
Markus Reinmöller ◽  
Lingxue Kong ◽  
Stanislav V. Vassilev ◽  
...  
Keyword(s):  
Prediction Model ◽  
Coal Ash ◽  
Flow Temperature

Intuitionistic Fuzzy Soft Ideals

2020 ◽  
pp. 91-104
Author(s):  
Shuker Khalil
Keyword(s):  
Social Science ◽  
Fuzzy Sets ◽  
Set Theory ◽  
Rough Sets ◽  
Real Life ◽  
Soft Sets ◽  
Vague Sets ◽  
Intuitionistic Fuzzy ◽  
Life Problems ◽  
Sets Theory

The basic notions of soft sets theory are introduced by Molodtsov to deal with uncertainties when solving problems in practice as in engineering, social science, environment, and economics. This notion is convenient and easy to apply as it is free from the difficulties that appear when using other mathematical tools as theory of theory of fuzzy sets, rough sets, and theory of vague sets. The soft set theory has recently gaining significance for finding rational and logical solutions to various real-life problems, which involve uncertainty, impreciseness, and vagueness. The concepts of intuitionistic fuzzy soft left almost semigroups and the intuitionistic fuzzy soft ideal are introduced in this chapter, and some of their basic properties are studied.

scholarly journals A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

2017 ◽  
Vol 14 (3) ◽  
pp. 455-489
Author(s):  
Inyeneobong Ekoi Edem ◽  
Sunday Ayoola Oke ◽  
Kazeem Adekunle Adebiyi
Keyword(s):  
Pattern Recognition ◽  
Industrial Accident ◽  
Recognition Model

scholarly journals Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks

2020 ◽  
Vol 10 (10) ◽  
pp. 3358 ◽  
Author(s):  
Jiyuan Song ◽  
Aibin Zhu ◽  
Yao Tu ◽  
Hu Huang ◽  
Muhammad Affan Arif ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Human Motion ◽  
Control Mode ◽  
Multilayer Perceptrons ◽  
Model Parameters ◽  
Motion Pattern ◽  
Accuracy Rate ◽  
Feature Parameters

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.

Information Entropy Based Fuzzy Pattern Recognition Model for Identification of Vulnerable Groups in Water Resource Conflicts

2014 ◽  
Vol 610 ◽  
pp. 316-319
Author(s):  
Li Feng Lv ◽  
Yan Ping Chen
Keyword(s):  
Pattern Recognition ◽  
Water Resource ◽  
Information Entropy ◽  
Water Rights ◽  
Liaoning Province ◽  
Vulnerable Groups ◽  
Recognition Model ◽  
Resource Conflicts ◽  
Fuzzy Pattern Recognition ◽  
Fuzzy Pattern

Identification of vulnerable groups in water resource conflicts is to improve the identification of vulnerable groups in the allocation of water rights and water markets water rights system. There are two difficulties: one is how to determine the weight of evaluation indexes; another is how to effectively deal with the subjectivity of the evaluation process and the low resolution. Therefore, this paper proposes “Information Entropy Based Fuzzy Pattern Recognition Model for Identification of Vulnerable Groups in Water Resource Conflicts (EFPQ-VRWC)” according to the fuzzy pattern recognition based on the combination of the maximum entropy principle and genetic algorithms. And identifying vulnerable groups of Daling River Basin in Liaoning Province, it illustrates the method of application value. And evaluation results have continuity, comparability and versatility so that can accurately reflect the level of vulnerable groups in water resource conflicts.

The Algorithm of Sense Disambiguation Based on Bayesian Model

2013 ◽  
Vol 427-429 ◽  
pp. 1879-1882
Author(s):  
Chun Xiang Zhang ◽  
Xue Yao Gao ◽  
Zhi Mao Lu
Keyword(s):  
Pattern Recognition ◽  
Bayesian Model ◽  
Ambiguous Word ◽  
Experimental Results ◽  
Accuracy Rate ◽  
Part Of Speech ◽  
Sense Disambiguation ◽  
The Right

Sense disambiguation is an important problem in pattern recognition. In this paper, a new algorithm of sense disambiguation is proposed, in which part-of-speech tags of the left word and the right word around the ambiguous word are extracted as discriminative features. At the same time, the bayesian model is selected as the sense disambiguation classifier and it is built based on discriminative features. The architecture of sense classification is given. The new algorithm is trained on sense-annotated corpus. Then it is used to determine its sense category. Experimental results show that the accuracy rate of disambiguation arrives at 60%.

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