… neural network is trained to predict breakout dimensions based on the input parameters of the
… The main finding of this section is a model that shows that among the parameters affecting …

… In addition, the influence of the structural parameters was determined using a set of 11
different Artificial Neural Network (ANN) algorithms, and a parametric study was performed …

… Typically, these parameters are chosen based on heuristic … the problem of choosing
appropriate parameters for the NN by … automatically and effectively searches the parameter space. …

… With regard to the overproduction phase, we exploited the conclusions of Partridge
concerning the most effective parameters in creating neural networks making different errors (see …

… of artificial neural networks, a stochastic time effective function … neural network – the stochastic
time effective neural network … So this implies that the appropriate volatility parameter is …

… more effective than adding extra parameters to the network. … Our method is the first to reallocate
free parameters effectively … during training is crucial to effective learning. Our work does …

… learning algorithm with different parameters such as architecture … different machine-learning
models such as neural networks [1], [2]… learners: decision trees, neural networks, k-nearest …

… We call these effectiveness of validation and effectiveness of … parameters that can be used
to guide the effective design of a NN. We examined the 27 studies that met our effectiveness …

… effective in their use of global information (Goldberg, 1989; Holland, 1975), they allow us to
consider a wide variety of networks … cycles effectively by continually considering networks to …

… neural networks. Sukthomya and Tannock [13] applied the Taguchi methodology to identify
the optimum combination of effective parameters … combination of effective parameters in ANN …