Sets of training and testing samples (Dtrain = {(x1,y1), (x2,y2), …, (xm,ym), yi R} and Dtest = {(xm+1,y m+1), (x m+2,y m+2), …, (xm+n,ym+n), yi R}, respectively) were obtained for the SVR network, which attempts to find a model f(x) in which y* = f(xi) and yi are as close as possible [54, 61]. For a maximum tolerable deviation between y* and yi of ϵ, the SVR problem can be formalized as

where C is the regularization constant and lϵ is the ϵ-insensitive loss function.

By introducing the slack variables ξi and ξ^i, Eq. (10) can be expressed as

The Lagrange multiplier ui can then be introduced to obtain the SVR solution as

where b is the model parameter to be determined and f(x) is the final model found by the SVR method.

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