Steady state concentrations were calculated using Mathematica's numerical root solving functions and solving the mass balance equations at steady state (error for each equation < 10-15) for the set of metabolite concentrations.
There are many algorithms that are used to construct or train networks to solve function approximation problems.
This paper presents a new evolutionary cooperative learning scheme, able to solve function approximation and classification problems with improved accuracy and generalization capabilities.
In this paper, a novel analytical approach is presented to solve function generation synthesis problems for planar four-bar linkages with no limitations on the number of precision points.
Although the PRP and HS methods are efficient, both of them have theoretical problems; thus, the number of solved function using the PRP formula does not exceed 75%.
In this paper, a design method is proposed to solve function-generation problems for four-bar linkages without any limitation on the number of precision positions, using variable length driving links.
Thus, the percentage value of solved functions using the HS+ formula is approximately 90%.
After solving the function, a set of accurate parameters of the NBTI model can be achieved.
Propositional Satisfiability (SAT) solvers are routinely used for solving many function problems.
In most cases, solving a function problem requires a number of adaptive or non-adaptive calls to a SAT solver.
