Gated Recurrent Unit with RSSIs from Heterogeneous Network for Mobile Positioning

Algorithm 1

Training of GRU.

(1)	Require: loss function J(W,b), initial parameter W, b
(2)	Normalize the features of input: equation (5)
(3)	While J not converged Do
(4)	Select k samples from the training set randomly
(5)	For each sample Do
(6)	Input layer: equation (6)
(7)	Hidden layer: equations (7)–(12)
(8)	Output layer: equation (13)
(9)	Compute the loss function: equation (14)
(10)	End For
(11)	Compute the gradients of weights
(12)	Update the weights
(13)	End While
(14)	Return W, b