Artificial Intelligence and Quantum Computing for Advanced Wireless Networks. Savo G. Glisic

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Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic


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i Endscripts normal w Subscript italic i j Superscript l Baseline normal a Subscript i Superscript l minus 1 Baseline left-parenthesis k right-parenthesis plus w Subscript b Superscript l Baseline equals sigma-summation Underscript i Endscripts s Subscript italic i j Superscript l Baseline left-parenthesis k right-parenthesis plus w Subscript b Superscript l Baseline comma"/>

      (3.23)StartFraction partial-differential s Subscript j Superscript l Baseline left-parenthesis k right-parenthesis Over partial-differential normal w Subscript italic i j Superscript l Baseline EndFraction equals StartFraction partial-differential left-parenthesis normal w Subscript italic i j Superscript l Baseline dot normal a Subscript i Superscript l minus 1 Baseline left-parenthesis k right-parenthesis right-parenthesis Over partial-differential normal w Subscript italic i j Superscript l Baseline EndFraction equals normal a Subscript i Superscript l minus 1 Baseline left-parenthesis k right-parenthesis period

      This holds for all layers in the network. Defining partial-differential upper J slash partial-differential s Subscript j Superscript l Baseline left-parenthesis k right-parenthesis equals delta Subscript j Superscript l Baseline left-parenthesis k right-parenthesis allows us to rewrite Eq. (3.21) as

      (3.24)normal w Subscript italic i j Superscript l Baseline left-parenthesis k plus 1 right-parenthesis equals normal w Subscript italic i j Superscript l Baseline left-parenthesis k right-parenthesis minus mu delta Subscript j Superscript l Baseline left-parenthesis k right-parenthesis dot normal a Subscript i Superscript l minus 1 Baseline left-parenthesis k right-parenthesis period

      We now show that a simple recursive formula exists for finding delta Subscript j Superscript l Baseline left-parenthesis k right-parenthesis. Starting with the output layer, we observe that s Subscript j Superscript upper L Baseline left-parenthesis k right-parenthesis influences only the instantaneous output node error ej(k). Thus, we have

      For a hidden layer, s Subscript j Superscript l Baseline left-parenthesis k right-parenthesis has an impact on the error indirectly through all node values s Subscript j Superscript l plus 1 Baseline left-parenthesis k right-parenthesis in the subsequent layer. Due to the tap delay lines, s Subscript j Superscript l Baseline left-parenthesis k right-parenthesis also has an impact on the error across time. Therefore, the chain rule now becomes

      where by definition partial-differential upper J slash partial-differential s Subscript m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis equals delta Subscript m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis. Continuing with the remaining term

      (3.27)StartFraction partial-differential s Subscript m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis Over partial-differential s Subscript j Superscript l Baseline left-parenthesis k right-parenthesis EndFraction equals StartFraction partial-differential s Subscript m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis Over partial-differential a Subscript j Superscript l Baseline left-parenthesis k right-parenthesis EndFraction StartFraction partial-differential a Subscript j Superscript l Baseline left-parenthesis k right-parenthesis Over partial-differential s Subscript j Superscript l Baseline left-parenthesis k right-parenthesis EndFraction equals StartFraction partial-differential s Subscript m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis Over partial-differential a Subscript j Superscript l Baseline left-parenthesis k right-parenthesis EndFraction f prime left-parenthesis s Subscript j Superscript l Baseline left-parenthesis k right-parenthesis right-parenthesis period

      Now

      (3.27a)StartFraction partial-differential s Subscript m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis Over partial-differential a Subscript j Superscript l Baseline left-parenthesis k right-parenthesis EndFraction equals StartFraction partial-differential s Subscript italic j m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis Over partial-differential a Subscript j Superscript l Baseline left-parenthesis k right-parenthesis EndFraction

      since the only influence a Subscript j Superscript l Baseline left-parenthesis k right-parenthesis has on s Subscript m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis is via the synapse connecting unit j in layer l to unit m in layer l + 1. The definition of the synapse is explicitly given as

      (3.28)s Subscript italic j m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis equals sigma-summation Underscript p equals 0 Overscript upper M Superscript l plus 1 Baseline Endscripts w Subscript italic j m Superscript l plus 1 Baseline left-parenthesis p right-parenthesis a Subscript j Superscript l Baseline left-parenthesis t minus p right-parenthesis period

      Thus

      (3.29)StartFraction partial-differential s Subscript italic j m Superscript l plus 1 Baseline left-parenthesis t right-parenthesis Over partial-differential a Subscript j Superscript l Baseline left-parenthesis k right-parenthesis EndFraction equals w Subscript italic j m Superscript l plus 1 Baseline left-parenthesis p right-parenthesis for t minus p equals k

      (3.30)equals StartLayout Enlarged left-brace 1st Row 1st Column w Subscript italic j m Superscript l plus 1 Baseline left-parenthesis t minus k right-parenthesis 2nd Column for 0 less-than-or-equal-to t minus k less-than-or-equal-to <hr><noindex><a href=Скачать книгу