Optimizing emission characteristics of a GDI engine using grey Taguchi methodology

2020 ◽  
Author(s):  
Nishant Kumar ◽  
Vinod Singh Yadav ◽  
Rahul Banerjee
Keyword(s):  
Emission Characteristics ◽  
Taguchi Methodology ◽  
Gdi Engine ◽  
Grey Taguchi

Optimization of neural network parameters using Grey–Taguchi methodology for manufacturing process applications

2014 ◽  
Vol 229 (14) ◽  
pp. 2651-2664 ◽  
Author(s):  
Dinesh Kumar ◽  
Arun Kumar Gupta ◽  
Pankaj Chandna ◽  
Mahesh Pal
Keyword(s):  
Neural Network ◽  
Depth Of Cut ◽  
Taguchi Methodology ◽  
Training Time ◽  
Network Parameters ◽  
The Neural Network ◽  
Trial And Error Method ◽  
Grey Relational ◽  
Grey Taguchi ◽  
Error Method

Performance of neural networks depends upon several input parameters. Several attempts have been made for optimization of neural network parameters using Taguchi methodology for achieving single objective such as computation effort, computation time, etc. Determination of optimum setting to these parameters still remains a difficult task. Trial-and-error method is one of the frequently used approaches to determine the optimal choice of these parameters. Keeping in view the problems with trial-and-error method, a systematic approach is required to find the optimum value of different parameters of neural network. In the present work, three most important distinct performance measures such as mean square error between actual and prediction, number of iteration, and total training time consumption have been probably considered first time concurrently. The multiobjective problem has been solved using Grey–Taguchi methodology. In this study, optimal combinations of different neural network parameters have been identified by using the Taguchi-based Grey relational analysis. The data set includes 81 sets of milling data corresponding to three-level full factorial experimental design for four cutting parameters, i.e. cutting speed, feed, axial depth of cut, and radial depth of cut, respectively. The output is average surface roughness for the experiment. The performance of different neural network models has been tabulated in L36 orthogonal array. Confidence interval has also been estimated for 95% consistency level to validate the optimum level of different parameters. It was found that the Taguchi-based Grey relational analysis approach can effectively be used as a structured method to optimize the neural network parameters settings, which can be easily implemented to enhance the performance of the neural network model with a relatively small size and time saving experiment. The result clearly indicates that the optimal combination of neural network parameters obtained by using the proposed approach performs better in terms of low mean square error, small number of iterations, and lesser training time required to perform the analysis which further results in lesser computation effort and processing time. Methodology proposed in this work can be utilized for any type of neural network application to find the optimum levels of different parameters.

Investigation of the combustion and particle emission characteristics of a GDI engine with a 50 MPa injection system

Fuel ◽  
2022 ◽  
Vol 315 ◽  
pp. 123079
Author(s):  
Diming Lou ◽  
Tong Wang ◽  
Liang Fang ◽  
Piqiang Tan ◽  
Zhiyuan Hu ◽  
...  
Keyword(s):  
Particle Emission ◽  
Injection System ◽  
Emission Characteristics ◽  
Gdi Engine

Influence of diluents on combustion and emission characteristics of a GDI engine

2017 ◽  
Vol 124 ◽  
pp. 746-755 ◽  
Author(s):  
Miaomiao Zhang ◽  
Wei Hong ◽  
Fangxi Xie ◽  
Yan Su ◽  
Liwei Han ◽  
...  
Keyword(s):  
Emission Characteristics ◽  
Gdi Engine
Sign in / Sign up

Export Citation Format

Share Document