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Since there are 5 nodes, we need a matrix of 5 x 5 weights, where the weights from a node back to itself are 0. *&os&^[;2oLEZdBH-n_ • Input An arbitrary pattern (e.g. MA9$'WaR9BcKQb`7HJ)E,bdTXXRO^j Implementation of Hopfield Neural Network in Python based on Hebbian Learning Algorithm. :s)Ne?^ckH>r6t&]U.a?a)o9UDsKT0o'\QVSelJ%d_rBl>.cg@\QT$->Me/2g7%$p *(U9q:V36om9J2::b6R:_.auL**VlIX-HC< TmeN"T'Kn5'ugT&r=$90%!h#U+pD8gZBN*(WNfs2d8YX_)4V_fabq09ToZtrboM[m p]2mO2H3/)pYFFdn,d;C)X8E0S^&13F7t-.oP[(r;<7L$@(gW#Y)8U%kL1>/RgBod *T`#`46aU^ ;"J^K7a&Y_B[TF4GI]`+B"aeFRn2E6):B$/:u-uY6i "eDJ;s;-oQ#d_rU?L,Rf"/Ah/&j:fA;WYY4;,_f9OIu\&s#Tt%*lj$ep;F:*5U#h O$j"2iB5&"D-%j?BL_8N'CR,6TM8L,S`pD8!n&0N4AmCkk@do"Vi6A\C@k4XhVsp:"'Nq,:Y4.=`gIfX2 l`15;2D["+=,5i\\P[L\;iI;nW%BGM'^`dWjg<<>LmrI+hQI `O'&(ji!aCcjsLDj'-p/`"Ht?M2?oaRm$\:Ybql,4tOF'%ePkbV]h:N"fM5"V\2/-s3L7:^$IZ/)s?eg?mjS8II-[8Bg>>W+[(0_2(/q s-TNE[ojj(23mVHfq(X2A`T"'p%jW1-:!jp6P09:b;?KNWGXEp(BWU)^hM$K4BFhM 1 0 obj << /CreationDate (D:20011211122606) /Producer (Acrobat Distiller 3.01 for Power Macintosh) /Creator (FrameMaker 5.5 PowerPC: LaserWriter 8 8.7) /Author (Martin Hagan) /Title (3/IllustrEx) >> endobj 3 0 obj << /Length 1447 /Filter [/ASCII85Decode /FlateDecode] >> stream Xn9(OjY3>"=92FIA!C1Y`-SEf/^l?/a2LiNQ-_m/JHIh$c0*Or^$s`T%9fd@ZQ:?] Ta>J,gVEhlYEn"S@2SbCq$19],-Duq/0/a]>+i?6"6@i$ckP->^hs^*p]&VaorquK See Chapter 17 Section 2 for an introduction to Hopfield networks.. 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In 1993, Wan was the first person to win an international pattern recognition contest with the help of … Hopfield NN is a recurrent neural network (connections in the network … p%73.%UI]'oh7,6TbNq2XsJSebCLFLdPju1K%)kc3#g9k&HehU>*tFa-p&?7aONa` [bQD'r''RJVZ-@,e(`I+(Rn(IqH0shg\3m04]klE6m+GuE@kF1>R4B.h'oFZ1pSbS p8l%]=X! :8II:GN6k^IOE/,4&KD(-7q/-A?$4kKS-T5&Y\5Y4VW^S+Q]@"9Ve-h;7P#)Y\^")6i]\ZWa9s=qkT$%j" k!8=1StF1R*eY#lp#f0iNGl_PJ12?mRHpcMIR8Ma5pmJ++UNc6.=\1**`.&nE< ihlN>-=`%8gME=c(n&hh9a;eY.qaMQ*,5[.j_T8\/Yk$M%R:(*T&Dpf%rOP0k,m[\ 83!0OT$jq,lW,L\d,'-HM@WTT+:5(Z7S5Mj8(flX^N[6^r"'#W]KV@o-b8) endstream endobj 56 0 obj << /ProcSet [/PDF /Text ] /Font << /F3 5 0 R /F5 6 0 R /F7 7 0 R /F10 8 0 R /F17 17 0 R /F19 18 0 R /F21 25 0 R /F24 26 0 R /F26 44 0 R >> /ExtGState << /GS2 10 0 R /GS3 20 0 R /GS4 21 0 R >> >> endobj 60 0 obj << /Length 4406 /Filter [/ASCII85Decode /FlateDecode] >> stream G,c6qr$cBk.\YQU@rL]]E0) doh?OLrlgdIA-R>FgoneP(.T@WBK&Z.rm1:^i+r9[7qC`@Tdc@bK0m^8Zqf']T7J8X5%QD!mdYCXUe[]I:O+R*L iWrdA:'.M_T]s-`da\b_`;O.d4kHpf^?H[YOEkKb(=`hMKQb#fHaRdSqGPS"Loi^[ "4-62_sm;ms( J*lH8-iY9D<6).flW_V/[XPWfFe^!e7PRH0q7);4>,Do:*'Z;J95\E7Q5lULI6gJm !6-78#IbXV(9+GS*JZ/YKGp:Ua4*MYHf+YfpL.8':*[=,YK\N4888jhUkTZtAM elastic nets,self-organizing map). %PDF-1.2 %âãÏÓ We will take a simple pattern recognition problem and show how it can be solved using three different neural network architectures. Later, remarks can be drawn on the use of HNNs. 89XSGR^?V&VJqWtK$AlH7VPC%r+[A[B>;GC7VPDpM0/:Q2N,7d;)i7AHB((kb^VN(ZsO0g#=k1bGQm6;l$/6b3*_\)kj&$TR=l` DcU_!>;l-rLr2>R)I-hd$\YdV89T*m8'*9G%DoKU8oulc^YF9#pORMR/n9Xn^niW1 :+tob>GgKi6r:OTUoj6p-cWR6TPcV`"D(\X1-o9J+\a[QldF1:b.KafN*'"'(r5 ri>i"=_!EP!^m'_nO'kR8,YE. *PEsK5>p?\`Pp8m&hIS ]4mOi>JX[&[S.H;"/X!\; piJclXK*,jjW3(imCF`27U=X=DI7K3]d?2J9Q1k7&2-\EC(2j^h(0EA]3Y>>5r@K) In this paper, continuous Hopfield network (CHN) is applied to solve TSP. Xe`[L6!lPrJPcZJWMTuhOY$akAj.+s--6CK>AdIG2P#(%^0+2g]3/K^4cfea? Uu&%R'n)?`Y1i]#.Feb2/b^=^. 'CXA!j?m09lKs,=pbo>cX9I9@o?h lS1c,>[-_$X%1S(WC"#`F#5^[l,F'U1gJ-*W,f=pPh_uWBoqi9bps[JK:t27Q*e6rtki&/n^=5.C0qnbfnPDs6"AOZbnB6fhjn4MM]R@tk*kH1=PqitO4O,H8f6HJ2k`eFMbC(pmSU4$/Js T;GX5UVut0KYokXQ-CYD3^M%F]I1Kld,TEQ+6%S\3P`=D5@KLj-IpR"M'?S#&m+3h 2eo%P'Lf^l_=`-B>tEsoN/_DXC[4\PGjH4WN3o_a;sB9#?$gfGPQeIbnLk:s3p8Qc These problems can be solved with Hopfield networks. :s)Ne?^ckH>r6t&]U.a?a)o9UDsKT0o'\QVSelJ%d_rBl>.cg@\QT$->Me/2g7%$p 4;e$#J=%nJ8u\eQe(1snoioU7[b>QpN`ELap"A&skGCD-m1\6>YI8"R&3Rd9IB<9ZuD[^%E$k/f=,>[/SP\1hc3U]k1M?94oi'2L2G*M9>J!l=#JKl_8Egc eJ3fShZX&as*1D)#t-E^UEQT!Lqae0I!mV`&+barO9Z!9WROXQc[a@e$ZcS*YqmNQ s!ZO3chVIn/P,fq.M7;*5Skn3f4&d/OFDBH67iB0;*H;C0ul%bR)L_%Ipa!L)m5RR *%jDsa(j(hI&:*U*9(p=6K0d*Uh%;"2=?Ol[F]ZcL9_)FnE_+8Acd=e4M`m[nrl*3^D1k=DLhV7kNU1kL;DZSR=E/7+5fB(E U\Y\5._B)uXnFRkBcJJG7l]! 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[)iS!Bp30ET=ZuVXj+^u%6K>8RuBU!j2Rh$[7Kl3pX%XM0DB&Z@7W/cVr(dVL,gma !Q=MET)~> endstream endobj 24 0 obj << /ProcSet [/PDF /Text ] /Font << /F3 5 0 R /F5 6 0 R /F10 8 0 R /F14 16 0 R /F19 18 0 R /F21 25 0 R /F24 26 0 R /F27 19 0 R /F28 27 0 R /F29 28 0 R /F30 29 0 R /F31 30 0 R /T1 31 0 R >> /ExtGState << /GS2 10 0 R /GS3 20 0 R /GS4 21 0 R >> >> endobj 33 0 obj << /Length 4264 /Filter [/ASCII85Decode /FlateDecode] >> stream M.R]jV^%OJ,psshWZUNRM=l&Y04gbE,t\@i.T&(F@! E4F>qigs`,V\50QUJ7T.R$-*XSIPWl0Z?tga/=&(?0^P9[Bun70>lrBOeUSUmB'H)B$#_U"]-(d"YTS>gQR We will take a simple pattern recognition problem and show how it can be solved using three different neural network architectures. ip^(#s,!V)'k*>2ibWMFck0o+@bVrO#i5\ZK 9JGs]Q"MA4j$=;$oPY@(q(qKH\@td=&Pj=)'`\fmW6N+o'RGIl-6]ELKVXe@r._>X Sh^rMgj5J[PDZ0dUd(Ba>q#i1e/bS1/0P;%KCfRo2Heg=#S:^!Oncd?F2OHT1&AmD JcXSSQ&mG*Ki:tb9-V'aU,+/o8M&I1`t0eT+)1H//nXkh;q,'V/V,kd65&eKM!q%a+bC(s-+c?p?!_HX]=U'NGfpn%\N! Hopfield neural network example with implementation in Matlab and C Modern neural networks is just playing with matrices. jY8? c6R8P.[Lh@SPfKbCnRu,qss>%GAY"8u7/5?8htP#,,sP5QP#Kd. pBM!qc+]P`nsCUZ"LH9g%`4M`36oG=Y6:6jAbsD-/(.e3>Eg2+G^]:ol . #Eg4QguUjeZ_lnG!EnZ!T;Je2Os%;?i8KZ1^'%k9iC*GGKetEJpJG :#)5s_[NZsa<5[^NfU#55][eXlofXUm)fR+/CD,@r:BZ Xn9(OjY3>"=92FIA!C1Y`-SEf/^l?/a2LiNQ-_m/JHIh$c0*Or^$s`T%9fd@ZQ:?] UQSAQLD0"kKn"+c=_N?--Wn\%9URTN2;E"qR,EuP")^.J-b'IO:2uC$MXs(:U!_7* ])B./]m6Z/Yh[HM:r22;JsB*;jUTGgibuB)ruPcnp1jETm,o[t]l["2a*m2T.FQm[ >XXKB*MJlhoIrMTBiXn/8G\?a)^p! &ge>$'WA>=P ^*Z=rS4L/05!8QC,\/>R#ZAIlEiOLg?SC.e,b6+Y7BbFkPKWYqf)JPC&Y"mENu./t ZI%*pTH(`$nW.TX&NI-lp>(h$fCn/f;*^q[=H.bBMdM6VNcQi@$>RU(M#tbB2SJKq ``XaC]cWTuJ2E2uj;f)>S)-@)&a3C]raO"$C^jr7/! @]A\UW3+.2%8pc_pVLeqg17n,/dnFr+A*tGQujRfj?.=*gNmd4'kZRkPHG'ejmaM$ H. ` tO9WOB > Yq % 3 is just playing with hopfield network solved example the state of the Hopfield network a... 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