A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

Table 1

A characteristic comparison of the existing methods with the proposed approach.

Method	TSP	TS	LE	DE	RI	TE	AA
Su and Hsieh [8]	Single response	Continuous	✓	✓		Neural network	—
Ko et al. [4]	Single response	Continuous	✓	✓		Neural network	—
Lo and Tsao [6]	Single response	Discrete	✓	✓		Neural network	—
Hsieh and Tong [13]	Multiple response	Continuous	✓			Neural network	—
Hsieh [14]	Multiple response	Continuous	✓			Neural network	—
Liao [9]	Multiple response	Discrete	✓	✓		Neural network	DEA
Chiang and Su [18]	Multiple response	Continuous	✓			Neural network	EDF
Antony et al. [12]	Multiple response	Discrete	✓	✓		Neuro fuzzy	MRS
Cheng et al. [21]	Multiple response	Continuous	✓			MANFIS	—
Lin et al. [20]	Multiple response	Discrete	✓	✓		Fuzzy rule base	MPS
Tarng et al. [26]	Multiple response	Discrete	✓	✓		Fuzzy rule base	MPS
Lu and Antony [19]	Multiple response	Discrete	✓	✓		Fuzzy rule base	MPS
Noorossana et al. [15]	Multiple response	Continuous	✓		✓	Neural network	DF
Chang and Chen [17]	Multiple response	Continuous	✓			Neural network	EDF
Gutiérrez and Lozano [11]	Multiple response	Discrete	✓	✓		Neural network	DEA
Chatsirirungruang [27]	Multiple response	Continuous	✓			Linear regression	LF
Sibalija and Majstorovic [23]	Multiple response	Continuous		✓		Neural network	GRA
Salmasnia et al. [25]	Multiple response	Continuous	✓	✓		ANFIS	DF
The proposed method	Multiple response	Continuous	✓	✓	✓	Neural network	WSN