Aero-Engine Adaptive Model Using Recursive Reduced Least Squares Support Vector Regression

2011 ◽  
Vol 65 ◽  
pp. 218-223
Author(s):  
Ling Ping Jiang
Keyword(s):  
Feature Selection ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Adaptive Model ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Sparse Model ◽  
Aero Engine ◽  
Selection Of

In consideration of the problem in traditional aero-engine adaptive model, a new algorithm was proposed based on Recursive Reduced Least Squares Support Vector Regression (RRLSSVR). Feature Selection of model input and flight envelope divided was needed before the model established, then adaptive model was developed in every small envelope. Finally, an adaptive model was applied to validate the effectiveness and feasibility of the proposed feature selection algorithm and sparse model using RRLSSVR.

scholarly journals Inspeção Automática de Defeitos em Madeiras de Pinus usando Visão Computacional

2008 ◽  
Vol 15 (2) ◽  
pp. 203-218
Author(s):  
Luiz E. S. Oliveira ◽  
Paulo R. Cavalin ◽  
Alceu S. Britto Jr ◽  
Alessandro L. Koerich
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Defect Detection ◽  
Color Image ◽  
Support Vector ◽  
Grayscale Image ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Vector Machines

This paper addresses the issue of detecting defects in Pine wood using features extracted from grayscale images. The feature set proposed here is based on the concept of texture and it is computed from the co-occurrence matrices. The features provide measures of properties such as smoothness, coarseness, and regularity. Comparative experiments using a color image based feature set extracted from percentile histograms are carried to demonstrate the efficiency of the proposed feature set. Two different learning paradigms, neural networks and support vector machines, and a feature selection algorithm based on multi-objective genetic algorithms were considered in our experiments. The experimental results show that after feature selection, the grayscale image based feature set achieves very competitive performance for the problem of wood defect detection relative to the color image based features.

AN EFFICIENT FEATURE SELECTION ALGORITHM FOR COMPUTER-AIDED POLYP DETECTION

2006 ◽  
Vol 15 (06) ◽  
pp. 893-915 ◽  
Author(s):  
JIANG LI ◽  
JIANHUA YAO ◽  
RONALD M. SUMMERS ◽  
NICHOLAS PETRICK ◽  
MICHAEL T. MANRY ◽  
...  
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Search Algorithm ◽  
Piecewise Linear ◽  
Least Square ◽  
Support Vector ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Polyp Detection ◽  
Computer Aided

We present an efficient feature selection algorithm for computer aided detection (CAD) computed tomographic (CT) colonography. The algorithm (1) determines an appropriate piecewise linear network (PLN) model by cross validation, (2) applies the orthonormal least square (OLS) procedure to the PLN model utilizing a Modified Schmidt procedure, and (3) uses a floating search algorithm to select features that minimize the output variance. The undesirable "nesting effect" is prevented by the floating search approach, and the piecewise linear OLS procedure makes this algorithm very computationally efficient because the Modified Schmidt procedure only requires one data pass during the whole searching process. The selected features are compared to those obtained by other methods, through cross validation with support vector machines (SVMs).

Sign in / Sign up

Export Citation Format

Share Document