Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

Table 3

Summary of fuzzy logic computational process.

Authors	Aim	Methods
Dalkili et al. [18]	To have a new algorithm-based ANFIS for tuning the path loss model	ANFIS
Supachai et al. [19]	To propose a multilayer fuzzy logic system (MLFS) for path loss prediction	Multilayer fuzzy logic system (MLFS)
Gupta et al. [20]	To propose a better method to predict path loss
Sanu et al. [21]	To proffer the use of a BPSK modulated signal to obtain the path loss	Fuzzy system + linear regression
Sumit et al. [22]	To introduce a fuzzy approach on the prediction of path loss	Mamdani fuzzy inference
Bhupuak and Tooprakai [23]	The use of K-means clustering and fuzzy logic for the minimization of prediction path loss error	K-means and fuzzy logic
Supachai and Pisit [24]	The use of new upper- and lower-bound models for the line-of-sight prediction of path loss in microwave systems	Fuzzy linear regression
Salman et al. [25]	Applied neuro-fuzzy model for the prediction of path loss	ANFIS
Gupta et al. [20]	Path loss prediction for current point of base station in a cellular mobile communications	Fuzzy logic
Surajudeen-Bakinde et al. [27]	Test ANFIS for path loss prediction	ANFIS
Danladi and Vasira [28]	Uses fuzzy logic and spline interpolation to modify the Hata model	Fuzzy logic
Shoewu et al. [29]	To develop a new propagation path loss model for different terrains in Lagos in the 900 MHz and 1800 MHz frequency bands	Fuzzy logic