Modelling of the Automatic Depth Control Electrohydraulic System Using RBF Neural Network and Genetic Algorithm

<table class="table-group" width="800px"><tr class="fig-image" id="a"><td><img alt="124014.fig.009a" src="https://static.hindawi.com/articles/mpe/volume-2010/124014/figures/124014.fig.009a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>Training RMS error with generations</td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="124014.fig.009b" src="https://static.hindawi.com/articles/mpe/volume-2010/124014/figures/124014.fig.009b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>Test RMS error with generations</td></tr></table></td></tr></table>

<table>Evolution of the RMS error on training data and test data.</table>

Mathematical Problems in Engineering

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Figure 9

Figure 9: Modelling of the Automatic Depth Control Electrohydraulic System Using RBF Neural Network and Genetic Algorithm 