The Bidirectional Optimization of Carbon Fiber Production by Neural Network with a GA-IPSO Hybrid Algorithm

Table 5

Errors of the proposed method, basic PSO-RNN, and conventional RNN.

Algorithms	MAE	MRE (%)	RMSE	TIC	Time(s)
Conventional RNN
1	173.1400	1876.58	341.7052	0.9612
2	1062.8000	1310.66	2004.3000	0.9391
3	151.6600	816.09	285.3062	0.9051
4	171.6800	264.56	285.1956	0.7416	1.7296
5	62.6800	443.07	103.5493	0.8054
6	34.8800	533.97	63.3625	0.9510
Total	276.1367	874.16	847.6972	0.9285
Basic PSO-RNN
1	1.9652	22.73	2.2049	0.1138
2	9.7604	11.30	10.3417	0.0590
3	2.5932	13.19	2.7487	0.0690
4	8.9433	15.27	10.2110	0.0806	0.4065
5	3.2098	20.66	3.2757	0.1054
6	0.4920	7.38	0.6731	0.0544
Total	4.4940	15.09	6.2559	0.0686
Proposed method
1	1.3996	14.37	1.7597	0.1026
2	8.2660	9.61	9.1148	0.0518
3	2.2693	11.04	2.5107	0.0647
4	8.0976	13.47	9.1473	0.0733	0.1464
5	2.9085	19.20	2.9776	0.0944
6	0.3260	4.94	0.4054	0.0317
Total	3.8778	12.11	5.5555	0.0612

Note: 1: viscosity average molecular weight, 2: conversion ratio, 3: solid content, 4: spinning jet drawing ratio, 5: coagulating bath temperature, 6: total drawing ratio.