Journals
Publish with us
Publishing partnerships
About us
Blog
International Journal of Aerospace Engineering
+
Journal Menu
Journal overview
For authors
For reviewers
For editors
Table of Contents
Special Issues
Submit
International Journal of Aerospace Engineering
/
2018
/
Article
/
Tab 12
Research Article
Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data
Table 12
Comparison of the feedforward neural network training algorithms for their test MSE value.
Training algorithm
Elapsed time (sec)
Training
R
Validation
R
Test
R
Training MSE
Test MSE
trainlm
4.203957
0.99767
0.99797
0.99719
62.8501
74.6465
traincgb
4.565835
0.99586
0.99299
0.99656
111.4932
91.2834
trains
10.047608
0.99561
0.99517
0.99653
118.4071
91.8717
trainrp
57.118179
0.99458
0.99297
0.9963
146.1263
97.9763
traincgp
3.699402
0.99401
0.99515
0.99501
161.8084
133.9647
trainscg
3.458904
0.99381
0.99084
0.99474
166.9843
139.1074
trainbfg
13.748363
0.99213
0.99176
0.99394
211.9474
160.1871
traincgf
7.667996
0.99179
0.99176
0.99395
222.3323
164.4613
trainoss
4.432661
0.99053
0.99197
0.99183
255.8224
215.3883
traingdx
3.614869
0.98838
0.98893
0.9893
312.1862
285.3237
traingda
4.008302
0.98312
0.98418
0.98733
459.2168
342.6505