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.008a" src="https://static.hindawi.com/articles/mpe/volume-2010/124014/figures/124014.fig.008a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>Traing RMS of KM and GA-RBF</td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="124014.fig.008b" src="https://static.hindawi.com/articles/mpe/volume-2010/124014/figures/124014.fig.008b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>Test RMS of KM and GA-RBF</td></tr></table></td></tr></table>

<table>Determination of number of hidden units of RBF neural network.</table>

Mathematical Problems in Engineering

fig8

Figure 8

Figure 8: Modelling of the Automatic Depth Control Electrohydraulic System Using RBF Neural Network and Genetic Algorithm 