Convergence and Stability of the Split-Step <svg style="vertical-align:-0.198pt;width:13.9375px;" id="M1" height="16.85" version="1.1" viewBox="0 0 13.9375 16.85" width="13.9375" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.022,-0,0,-.022,.625,15.975)"><path id="x1D73D" d="M555 476q0 -127 -51 -241q-50 -114 -129 -180t-163 -66q-80 0 -124 60.5t-44 165.5q0 114 47 225q46 108 124 177q77 68 159 68q84 0 132.5 -55t48.5 -154zM184 359h243q13 65 13 128q0 87 -14.5 122.5t-51.5 35.5q-58 0 -110 -85q-53 -85 -80 -201zM417 315h-245
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<table> Mean-square stability comparison of SST and SSTM for the SDHNNs (<a href="https://static.hindawi.com/articles/aaa/volume-2013/169214/figures/#EEq5.1">43</a>), Case <a href="https://static.hindawi.com/articles/aaa/volume-2013/169214/figures/#casee2">2</a>, <svg height="13.55" id="M259" style="vertical-align:-0.1638pt" version="1.1" viewbox="0 0 49.325001 13.55" width="49.325001" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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</svg>.</table>

Abstract and Applied Analysis

fig5

Figure 5

Figure 5: Convergence and Stability of the Split-Step <svg style="vertical-align:-0.198pt;width:13.9375px;" id="M1" height="16.85" version="1.1" viewBox="0 0 13.9375 16.85" width="13.9375" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.022,-0,0,-.022,.625,15.975)"><path id="x1D73D" d="M555 476q0 -127 -51 -241q-50 -114 -129 -180t-163 -66q-80 0 -124 60.5t-44 165.5q0 114 47 225q46 108 124 177q77 68 159 68q84 0 132.5 -55t48.5 -154zM184 359h243q13 65 13 128q0 87 -14.5 122.5t-51.5 35.5q-58 0 -110 -85q-53 -85 -80 -201zM417 315h-245
q-15 -71 -15 -132q0 -154 70 -154q57 0 107 83q50 84 83 203z" /></g></svg>-Milstein Method for Stochastic Delay Hopfield Neural Networks 

Abstract and Applied Analysis

Convergence and Stability of the Split-Step -Milstein Method for Stochastic Delay Hopfield Neural Networks

Figure 5