SBGC-LSTM io gathcr dynamic data.En. Eurygasieri's
location in the D-dimensional scarching spacc
denoted
wih the
by the cxpresslon
optinization, solution The fommula for Eurygaster >peed
Each Eurygaster moves a
(51,52,
dhflcrent speed as they approach the cxlreme global value. Furthemmore.S, = (s,1, 5,2,.... C TEMPORAL HIERARCHICAL ARCHITECTURE
Pollowing LSTM layers, SBGC-LSTM layers receive scnes
where AT E
of augmcnted node fcatures
Rvd,. Three SBGC-LSTM layers are slacked in the pro-
posed moxdcl to leam the temporal dynamics and spatial
arrangement We creaie a lcmporal hicrarchical design of
SBGC-LSTM with average pools in icmporal domains and
inspired by spatial pooling o CNNS. Due
lemporal
hierarchical architecture, input temporal receplive ficlds
of top SBGC-LSTM layers become short-term fasteners
and are more sensitive to lcmporal dynamics. Morcover. they drastically reduce computing costs while cnhancing
periomances [27).Particlc swarm optimization (PSO) and foraging icchniques
are used in EOA optimizations, which are modelled afier
curygasters: Based on the obseration that curygastcrs use
thcir anicnnae to scan their sumoundings, this is truc.Thesc
paramcters are initially set to high values, which decrease
progressively: as a result, one trics t0 atlain a wide region
helore rcducing to obiain a capacity that is reasonable ior
Eurygasicr.The mark for the correctly dctecied position is qn.
whereas the lefi detccicd position is denoicd by qk.
These places include food flavor, which is represenied
by the fitness function values /(qn) and /(qn), which were
compulcd using the recommended method.The EOA aligorithm's location and
speed updating procedure is as follows:
atlats 820
where in Eq (14), (A/), is the cxpansion charactcristic of
combination i at time t. The main point here is that the
lincar portion and LSTM are distnbutcd among several
charaeteristics.I) LEARNING SBGC-LSTM
AE the cnd, the GF and
of time stamp ane convert
as resulis of and (owr); for CI phunses, where (ou),
(ou ),.(ox)a.....Thcrefore, to remove the scale
viriation between the two [eatures, an LSTM layer was
applicd:
EM =fomlconcara, Va) = fmlconcarn -fi-1)
(14)
the decaying weight coefficicnts.