Fishes Go MOO: Neural Network Data Prediction

Neural Network Prediction: NN scripts and data by N.A. Battista Cite: N.A. Battista, Fishes Go MOO: Pareto analysis for speed and cost of transport across a 6-dimensional design space. ______ (2025) DOI ----------------------------------------------------------------------------------------- MATLAB SCRIPTS: | |--> Prediction_LinePlots.m: Uses the Neural Network to predict speeds across the(f,Tamp)-subspace, ie, predicts speeds across a 2-D slice out of the overall 6-D parameter space | |--> Provides plots of the speeds across particular slices. |--> As frequency and tail beat amplitude vary, the other 4 input parameters are held constant. | |--> Prediction_Errors_Speed.m Calculates the relative errors btwn the simulated speed values and those predicted via the Neural Network across both the training and test datasets | |--> Prints error statistics to the command window |--> Provides a qualitative comparison plot |--> Provides histograms of the PDF and CDF for the relative errors | |--> Each script is self-contained. That is they contain all the necessary supporting functions in order to run, e.g., forward propagation, activation function, scaling functions, etc ----------------------------------------------------------------------------------------- DATA PROVIDED | |--> Trained_Neural_Network.mat |--> Contains the trained weight matrices and bias vectors for the NN | |--> TRAINING_and_TEST_data.mat |--> Contains the input parameters and speed values for both the training and test datasets |--> speed data is given in both the simulation values as well as the transformed values for the NN (ie, those after the Box-Cox transforms and standardization) | |--> SCALING_INFO.mat |--> Provides the data transformation parameters for transforming the data into and out of the NN's worldview (ie, the Box-Cox transform params, standardization params, etc)