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However, the existing ISPH method directly approximates the second order derivatives of the functions to be solved by using the Poisson equation.
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The latter allows different objective functions to be solved based on various constraints set by process and design engineers, prior to detailed design exercises.
An activity function, which needs to be solved by ANN, is selected.
This is unconstrained optimization problem with respect to scalar function, which can be solved by the Newton method (see [11]).
Other useful utility functions can also be solved by DCA-based algorithms (see [24]).
The optimal function can be solved by sequential quadratic programming (SQP).
Here, Γ is the gamma function and can be solved by following equation: {Gamma}_{(x)}={int}_0^{infty }{e}^{-upmu}{u}^{x-1} du (13).
But when Y ∈ RM × 1 is K-sparse signal, Function (2) can be solved by the l0-norm minimization based on CS [9, 10].
According to Newton's iteration principle, it can be assumed that the single-factor evaluation criteria (r chi )) are a linear piecewise function, which can be solved by successive approximation.
The linear separability constraint which is certainly difficult to ensure in realistic situations, can be solved by using kernel functions to transform the input space to a higher dimensional RHKS [33].
Regarding this definition as the cost function, the reconstruction problem can be solved by an optimization method.
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