Artificial neural networks with conformable transfer function for improving the performance in thermal and environmental processes.

This research proposes a novel transfer function based on the hyperbolic tangent and the Khalil conformable exponential function. The non-integer order transfer function offers a suitable neural network configuration because of its ability to adapt. Consequently, this function was introduced into neural network models for three experimental cases: estimating the annular Nusselt number correlation to a helical double-pipe evaporator, the volumetric mass transfer coefficient in an electrochemical reaction, and the thermal efficiency of a solar parabolic trough collector. We found the new transfer function parameters during the training step of the neural networks. Therefore, weights and biases depend on them. We assessed the models applied to the three cases using the determination coefficient, adjusted determination coefficient, and the slope-intercept test. In addition, the MSE for the training set and the whole database were computed to show that there is no overfitting problem. The best-assessed models showed a relationship of 99%, 97%, and 95% with the experimental data for the first, second, and third cases. This novel proposal made reducing the number of neurons in the hidden layer feasible. Therefore, we show a neural network with a conformable transfer function (ANN-CTF) that learns well enough with less available information from the experimental database during its training.