    % Training
    lolimotObject = lolimot;                   % Generate an empty net and data set structure

    u = linspace(0,1,300)';
    lolimotObject.input = u;
    lolimotObject.output = 1 ./ (0.1 + u);

    lolimotObject.maxNumberOfLM = 6;                            % Termination criterion for maximal number of LLMs
    lolimotObject.minError = 0.005;                             % Termination criterion for minimal error
    lolimotObject.kStepPrediction = 0;                          % Static model
    lolimotObject.smoothness = 0.8;                             % Less overlap between the validity functions
    lolimotObject.complexityPenalty = 1;                       % Increase penalty for model complexity
    lolimotObject.history.displayMode = true;                   % display information

    lolimotObject.LOOCV = true;

    % Train net
    lolimotObject = lolimotObject.train;

    % Find best performance/complexity trade-off
    [~, idxBest] = min(lolimotObject.history.penaltyLossFunction);
    lolimotObject.leafModels = lolimotObject.history.leafModelIter{idxBest};

    % Generalization
    uG = linspace(-0.05,1.2,270)';
    yG = 1 ./ (0.1 + uG);

    % Simulate net
    yGModel = calculateModelOutput(lolimotObject, uG, yG);
    JG = calcGlobalLossFunction(lolimotObject ,yG, yGModel);

    figure
    lolimotObject.plotModel

    figure
    lolimotObject.plotPartition

    Input scaling complete.

    Initial net has 1 local linear model(s): J = 0.558870.

    1. Iteration. Number of local linear models = 1. Checking for split of model 1 ...
       Testing split in dimension 1 with ratio 0.50: J = 0.338520.
    -> Splitting in dimension 1 with ratio 0.50: J = 0.338520.

    2. Iteration. Number of local linear models = 2. Checking for split of model 2 ...
       Testing split in dimension 1 with ratio 0.50: J = 0.168858.
    -> Splitting in dimension 1 with ratio 0.50: J = 0.168858.

    3. Iteration. Number of local linear models = 3. Checking for split of model 4 ...
       Testing split in dimension 1 with ratio 0.50: J = 0.070777.
    -> Splitting in dimension 1 with ratio 0.50: J = 0.070777.

    4. Iteration. Number of local linear models = 4. Checking for split of model 6 ...
       Testing split in dimension 1 with ratio 0.50: J = 0.032529.
    -> Splitting in dimension 1 with ratio 0.50: J = 0.032529.

    5. Iteration. Number of local linear models = 5. Checking for split of model 8 ...
       Testing split in dimension 1 with ratio 0.50: J = 0.025310.
    -> Splitting in dimension 1 with ratio 0.50: J = 0.025310.

    Maximum number of local models reached.

    Final net has 6 local linear models: J = 0.025310.

    Net 6 with 6 LLMs is suggested as the model with the best complexity trade-off.

    Plot model for dimension 1.