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