Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms

Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine

“…With this improvement, a straightforward programming pattern can be used for the genotype of any individual throughout the population, as previously suggested [33,37,38]. The programming scheme is presented in Figure 1.…”

“…Note that this technique is iterated for every neuron of the further hidden layer accompanied by the connectivity structure of the NN. Such an answer from standard equations is more exactly vulnerable to improve deviations and, more outstandingly, to boost the individuality of the aforementioned formulas [37][38][39][40][41][42].…”

“…i.e., topology identification and the parametric [37][38][39][40][41][42] utilization of the GA for designing the structure for GMDH type of NNs. Stochastic techniques are generally utilized throughout the process of training of NNs in terms of connected coefficients or weights, and have been effectively implemented and demonstrated to be superior to conventional gradient-based approaches.…”

“…The process of selection is performed on the basis of a natural roulette wheel selection approach for selecting two parents generating two offsprings [33,37,38].…”

Convergence Acceleration Operator for Multiobjective Optimization

“…Given their generational population-based approach, EAs require a significant number of objective function calculations to be performed. The use of approximated models using neural networks (NNs), or other metamodelling techniques, such as Kriging-based approximations, or response surface models [6], [7], provides low computational burden alternatives to full objective function evaluation [8], [9]. Metamodeling is a wellestablished research discipline that focuses on building approximated models which reduce the computational effort needed to compute exact and expensive objective functions.…”