Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR

<table class="table-group" width="800px"><tr class="fig-image" id="a"><td><img alt="192021.fig.009a" src="https://static.hindawi.com/articles/ddns/volume-2013/192021/figures/192021.fig.009a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>FDP and LS-SVR algorithms</td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="192021.fig.009b" src="https://static.hindawi.com/articles/ddns/volume-2013/192021/figures/192021.fig.009b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>Kay and LS-SVR algorithms</td></tr></table></td></tr></table>

<table>Impact of <svg height="12.425" id="M235" style="vertical-align:-0.1092pt" version="1.1" viewbox="0 0 16.1 12.425" width="16.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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</svg> on MSE performance.</table>

Discrete Dynamics in Nature and Society

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Figure 9

Figure 9: Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR 