The computed coefficient of determination for the training and test phases of the best artificial neural network-genetic algorithm model was obtained at 0.9696 and 0.9672, respectively.
Hence, parameters such as the coefficient of determination for the training set (R2training) that measure the fitting ability of a model play only a minor role, typically unrelated to predictive power.
The Taguchi orthogonal array is employed for the operating points determination of training data; then, based on the same training data, which contain only 25 samples, the predictive performances of ANN and SVM are evaluated and compared.
The best statistical coefficient of multiple determinations (R2-value) for training data equals to 0.999715, 0.995627, 0.999497, and 0.997648 obtained by different algorithms with seven neurons for the non-dimensional exergy losses of evaporator, generator, absorber and condenser, respectively.
Determination coefficient (R2) values for training and testing data sets in the prediction of bonding strength by ANN were 0.997 and 0.986, respectively.
Mean absolute percent error (MAPE) of 1.81% and coefficient of determination (R2) of 0.9976 for training data and MAPE of 1.52% and R2 value of 0.9948 for testing data were obtained.
The obtained correlation of determination of 0.966 and 0.944 for training and testing datasets show the applicability of the proposed model to predict shale shear strength parameters with high accuracy.
Frames, or dictionaries, for sparse signal representations may be designed using an iterative algorithm with two main steps: (1) Frame vector selection and expansion coefficient determination for signals in a training set, selected to be representative of the signals for which compact representations are desired, using the frame designed in the previous iteration.
The prediction performance of the proposed ANFIS model (coefficients of determination equal to 0.989 and 0.960 for training and testing data, respectively) reveals the feasibility of the model.
Coefficient of determination (R 2) of 0.99985 and 0.99505 are acquired for training data set of As III) and As V), respectively.
