This edited publication provides medical result of the thirteenth IEEE/ACIS foreign convention on computing device and knowledge technology (ICIS 2014) which was once hung on June 4-6, 2014 in Taiyuan, China. the purpose of this convention used to be to assemble researchers and scientists, businessmen and marketers, lecturers, engineers, desktop clients, and scholars to debate the varied fields of desktop technological know-how and to percentage their stories and alternate new rules and data in a significant method. study effects approximately all features (theory, functions and instruments) of laptop and data technological know-how, and to debate the sensible demanding situations encountered alongside the way in which and the options followed to unravel them.

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M) 2 wql (k) = η2 ∂(x j (k)) h . δ (k) 2 (k) j ∂wql r = η2 n w1jq (k)wi5j (k) f j′ (. ) q=1 j=1 ⎛ ⎞ ∂ yc,l (k−1) 2 ql (k) ⎜ ∂w ⎟ o n n ×⎝ . δ (k) ∂ xc,h (k−1) five (k) ∂ x j (k−1) ) ⎠ i +( wi4h (k) ∂w + w 2 (k) 2 (k) ij ∂w ql ql h=1 j=1 γ (l = 1, . . . , m) w1jq (k) = η1 ∂(x j (k)) h . δ (k) = η1 ∂w1jq (k) j ∂ x j (k) = (u q (k) + ∂w1jq (k) n wi5j (k) f j′ (. ) j=1 m l=1 ∂(x j (k)) o . δ (k) ∂w1jq (k) i r 2 wql (k)yc,l (k)) + ( q=1 (15) m w1jq (k). A1 + w3jl (k). B1 ) l=1 (16) (q = 1, . . . , r ) the place A1 = m l=1 B1 = (γ 2 (k)(γ wql ∂ yc,l (k−1) ∂w1jq (k) ∂ yc,l (k−1) ∂w1jq (k) +( n h=1 +( n h=1 wi4h (k) ∂ xc,h (k−1) + ∂w1jq (k) ∂ xc,h (k−1) wi4h (k) ∂w 1 (k) + jq n j=1 n j=1 wi5j (k) ∂ x j (k−1) ))) ∂w1jq (k) ∂ x (k−1) wi5j (k) ∂wj 1 (k) )) jq which shape the educational set of rules for the OIFHO ENN, the place η1 , η2 , η3 , η4 , and η5 are studying premiums of W 1 , W 2 , W three , W four , and W five , respectively, and δio (k) = (yd,i (k) − yi (k))gi′ (. ) (17) 178 F. Nejadmorad Moghanloo et al. m δ hj (k) = n δqi (k) = w5ji (k)δio (k) f j′ (. ) (18) i=1 m w1jq (k)w5ji (k)δio (k) f j′ (. ) (19) j=1 i=1 if g(x)is taken as a linear functionality, then g ′ (. ) = 1. three Convergence of Output to enter suggestions, Hidden to Output Elman Neural community (OIFHO ENN) The replace ideas in Eqs. (12–16) desire acceptable number of the educational charges. For the educational price with a small price, the convergence might be assured, however the velocity of convergence is particularly gradual. however, if the price of the educational fee is simply too huge, the set of rules turns into risky [2]. with a purpose to teach neural networks successfully, we advise 5 criterions of choosing right studying premiums for the dynamic again propagation set of rules adaptively in keeping with the discrete-type Lyapunov balance research. the next theorems provide enough stipulations for the convergence of OIFHO ENN. think that the amendment of the weights of by means of Eqs. (12–16). For the convergence of OIFHO ENN we've got the next theorems. Theorem 1 The strong convergence of the replace rule on W 1 is assured if the educational fee η1 (k) satisfies the subsequent : eight zero < η1 (k) < nr max(wi5j (k)) ij max u q (k) + max q q m p=1 (20) wq2 p yc, p (k) facts outline the Lyapunov power functionality as follows: E(k) = 1 2 m ei2 (k) (21) i=1 the place ei (k) = yd,i (k) − yi (k) (22) and accordingly, we will be able to receive the amendment of the Lyapunov power functionality E(k) = E(k + 1) − E(k) = 1 2 m i=1 [ei2 (k + 1) − ei2 (k)] (23) A New transformed Elman Neural community with sturdy studying Algorithms … 179 the mistake in the course of the studying procedure may be represented as n ei (k + 1) = ei (k) + m ∂ei (k) w1jq = ei (k) − 1 ∂w jq j=1 q=1 n m ∂ yi (k) w1jq 1 ∂w jq j=1 q=1 (24) accordingly m ∂ yi (k) T ∂ yi (k) 2 2 ] [ ] −1 ∂W1 ∂W1 i=1 ⎡ ⎤ m m 2 2 ∂ y 1 (k) i ⎦=− = ei2 (k) ⎣ 1 − η1 (k) − 1 ei2 (k)βi1 (k) 2 ∂W1 E(k) = 1 2 ei2 (k) 1 − η1 (k)[ i=1 i=1 (25) the place βi1 (k) = 1 2 1 − 1 − η1 (k) = 21 η1 (k) ∂ yi (k) ∂W1 2 ∂ yi (k) ∂W1 2 2 2 − η1 (k) ∂ yi (k) ∂W1 (26) 2 we've got ∂ yi (k) ∂w1jq = ∂ yi (k) ∂ x j (k) ∂ x j (k) .