Title :
An Algorithm for Selecting RBF-ANN Centers of Species Mass in Engine
Author :
Guo, Xiao-Ping ; Wang, Zhan-Jie
Author_Institution :
Sch. of Energy & Power Eng., Dalian Univ. of Technol., Dalian, China
fDate :
March 31 2009-April 2 2009
Abstract :
After analyzing the features of species mass in engine combustion process, considering the accuracy and calculating time together, based on a network for one specie, this paper proposes an algorithm for selecting RBF-ANN centers with sectional method to apply in engine combustion species mass: in the image of species mass fraction-temperature, mass fraction area is divided into N parts dynamically, temperature area is divided into 6 fixed parts, then selects a sample as RBF-ANN center in every overlapped area of vertical (mass fraction) area and horizontal (temperature) area. Through calculating 135 diesel combustion process, it shows that the accuracy of function approximation has been improved greatly with sectional method, compared with subtractive cluster method.
Keywords :
diesel engines; function approximation; mechanical engineering computing; neural nets; radial basis function networks; RBF-ANN; diesel combustion process; engine combustion process; engine combustion species mass; function approximation; sectional method; Accuracy; Algorithm design and analysis; Combustion; Diesel engines; Function approximation; Image analysis; Temperature; Artificial neural network; Chemical species; Combustion; Function approximation; RBF;
Conference_Titel :
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-0-7695-3507-4
DOI :
10.1109/CSIE.2009.374
