Headliner Absorption Parameter Prediction and Modeling

2015 ◽  
Author(s):  
Katherine Tao ◽  
Alan Parrett ◽  
David Nielubowicz
Keyword(s):  
Absorption Parameter ◽  
Parameter Prediction

Parameter Prediction Based on Type-2 Fuzzy Clustering

2018 ◽  
Vol 26 (06) ◽  
pp. 877-892 ◽  
Author(s):  
Türkan Erbay Dalkiliç ◽  
Seda Sağirkaya
Keyword(s):  
Fuzzy Logic ◽  
Regression Analysis ◽  
Fuzzy Clustering ◽  
Alternative Methods ◽  
Data Set ◽  
Clustering Problem ◽  
Fuzzy Clustering Method ◽  
Parameter Prediction

In regression analysis, the data have different distributions which requires to go beyond the classical analysis during the prediction process. In such cases, the analysis method based on fuzzy logic is preferred as alternative methods. There are couple important steps in the regression analysis based on fuzzy logic. One of them is identification of the clusters that generate the data set, the other is the degree of memberships that are determined the grades of the contributions of the data contained in these clusters. In this study, parameter prediction based on type-2 fuzzy clustering is discussed. Firstly, type-1 fuzzy clustering problem was solved by the fuzzy c-means (FCM) method when the fuzzifier index is equal to two. Then the fuzzifier index m is defined as interval number. The membership degrees to the sets are determined by type-2 fuzzy clustering method. Membership degree obtained as a result of clustering based on type-1 and type-2 fuzzy logic are used as weight and parameter prediction using these membership degrees that determined by the proposed algorithm. Finally, the prediction result of the type-1 and type-2 fuzzy clustering parameter is compared with the error criterion based on the difference between observed values and the predicted values.

A hybrid onboard adaptive model for aero-engine parameter prediction

2020 ◽  
Vol 105 ◽  
pp. 105951
Author(s):  
Shuwei Pang ◽  
Qiuhong Li ◽  
Hailong Feng
Keyword(s):  
Adaptive Model ◽  
Aero Engine ◽  
Parameter Prediction

scholarly journals Intelligent Prediction of Transmission Line Project Cost Based on Least Squares Support Vector Machine Optimized by Particle Swarm Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Yi ◽  
Hao Zheng ◽  
Yu Tian ◽  
Jin-peng Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Transmission Line ◽  
Least Squares ◽  
Principal Component ◽  
Support Vector ◽  
Swarm Optimization ◽  
Empirical Parameter ◽  
Parameter Prediction

In order to meet the demand of power supply, the construction of transmission line projects is constantly advancing, and the level of cost control is constantly improving, which puts forward higher requirements for the accuracy of cost prediction. This paper proposes an intelligent cost prediction model based on least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO). Originally extracting natural, technological, and economic indexes from the perspective of cost composition, principal component analysis (PCA) is used to reduce the dimension of indexes. And PSO is innovatively introduced to optimize the parameters of LSSVM model to obtain the optimal parameters. The obtained principal component data are imported into empirical parameter LSSVM prediction model and the optimized parameter PSO-LSSVM prediction model, respectively, for modeling and prediction, and then comparing the prediction results to analyze the effect of model optimization. The results show that the absolute deviation of the optimized parameter prediction model is less than 9%. And the prediction accuracy of the optimized parameter prediction model is better than that of the empirical parameter model, which can provide a reliable basis for investment decision-making of transmission line projects.

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