... given bythe following system, with i∗input variables, k∗neurons in the first hiddenx1x2x3p1p2Inputs - x Hidden Layer - 1 neurons - n1,n2n1yHidden Layer - 2neurons - p1,p2n2OutputFIGURE ... form:Lt=Tt =1 1 2πσ2texp−(yt− yt)22σ2t(2 .10 )yt= α +βσt(2 .11 )t= yt− yt(2 .12 )σ2t=δ0+δ 1 σ2t 1 +δ22t 1 (2 .13 )where the symbols ... ωk,0+i∗i =1 ωk,ixi,t(2.48)Nk,t= 1 1+e−nk,t(2.49)pl,t= ρl,0+k∗k =1 ρl,kNk,t(2.50)Pl,t= 1 1+e−pl,t(2. 51) yt= γ0+l∗l =1 γlPl,t(2.52)It should be clear that adding a second hidden layer increases...