Algorithm 1: Pseudocode of NN-BLMA.

Starting of NN-BLMAConstruction: Construct inputs and reference data set using RK-4 method in Mathematica Data selection: Input and target data must be selected in non-linear format, i.e., matrices. Startup: Taking number of neurons and distributing the reference data set into training, testing and validation 60 Hidden neurons $60\%$ data for training $20\%$ data for testing $20\%$ data for validation Architecture: Each input is given a weight, and the input to the transfer function is formed by adding the weights of all of the inputs together along with the bias. Stopping criteria: If all of the conditions listed below are met, the previous process will end automatically. Mu reach to its maximum value Number of iteration reaches to maximum Performance value reaches to minimum Validation’s performance became less then maximum fail Gradient’s performance dropped below minimum gradient The network is generalised using training data. If the outputs are good, proceed to Saving Output; otherwise, retrain the network. Retraining: Change the startup parameters and train the network again Saving outputs: End the process by saving the results graphically as well as numerically Ending of NN-BLMA