WSNs Compressed Sensing Signal Reconstruction Based on Improved Kernel Fuzzy Clustering and Discrete Differential Evolution Algorithm

Pseudocode 1

(1) Set the original number of clusters and

initialize parameters: maximum number of clusters

, fuzzy weighted index , maximum number of

iterations , and the stopping criterion .

(2) Initialize the clustering center according to the

“maximum benefit” clustering center initialization

method. Calculate the initial membership matrix

according to (9).

For

(3) While () do

(4) membership matrix and clustering

center according to (9).

(5) .

(6) Calculate validity index according to

(10)-(13).

End for

(7) Output results: the corresponding to the minimum

is the optimal number of clusters, and the

corresponding clustering center and membership is the

optimal and respectively.