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Bidirectional associative memory

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The internal matrix has n x p independent degrees of freedom, where n is the dimension of the first vector (6 in this example) and p is the dimension of the second vector (4). This allows the BAM to be able to reliably store and recall a total of up to min(n,p) independent vector pairs, or min(6,4)
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To retrieve the association A1, we multiply it by M to get (4, 2, -2, -4), which, when run through a threshold, yields (1, 1, 0, 0), which is B1. To find the reverse association, multiply this by the transpose of M.
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and hetero-associative. BAM is hetero-associative, meaning given a pattern it can return another pattern which is potentially of a different size. It is similar to the
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defines the state of a BAM. To store a pattern, the energy function value for that pattern has to occupy a minimum point in the energy landscape.
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The layers can be connected in both directions (bidirectional) with the result the weight matrix sent from the X layer to the Y layer is
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proved to correspond to a local minimum of the energy function. The discrete BAM is proved to converge to a stable state.
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It is said to be bi-directional as it can respond to inputs from either the input or the output layer.
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NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHM: SYNTHESIS AND APPLICATIONS (WITH CD)
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is presented to BAM, the neurons change states until a bi-directionally stable state
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Bidirectional Associative Memory – Python source code for the Wiki article
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and the weight matrix for signals sent from the Y layer to the X layer is
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The stability analysis of a BAM is based on the definition of
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Imagine we wish to store two associations, A1:B1 and A2:B2.
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Bidirectional associative memories – ACM Portal Reference
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RAJASEKARAN, S.; PAI, G. A. VIJAYALAKSHMI (2003-01-01).
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for the bidirectional case, which for a particular case
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The memory or storage capacity of BAM may be given as
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in 1988. There are two types of associative memory,
149:These are then transformed into the bipolar forms: 901:IEEE Transactions on Systems, Man, and Cybernetics 848: 787: 761: 697: 651: 619: 587: 560: 516: 496: 476: 425: 238: 206: 117: 90: 177: 456: 156:X2 = (1, 1, 1, -1, -1, -1), Y2 = (1, -1, 1, -1) 153:X1 = (1, -1, 1, -1, 1, -1), Y1 = (1, 1, -1, -1) 504:" is the number of units in the X layer and " 8: 797:Hopfield's Auto-associative Energy Function 145:A2 = (1, 1, 1, 0, 0, 0), B2 = (1, 0, 1, 0) 142:A1 = (1, 0, 1, 0, 1, 0), B1 = (1, 1, 0, 0) 840: 804: 774: 753: 717: 712:The Energy Function proposed by Kosko is 686: 673: 664: 632: 600: 580: 541: 524:" is the number of units in the Y layer. 509: 489: 454: 265: 253: 230: 225: 219: 207:{\displaystyle M=\sum {\!X_{i}^{T}Y_{i}}} 197: 187: 182: 176: 165: 109: 103: 83: 887: 885: 881: 7: 951: 949: 924: 922: 894:"Bidirectional Associative Memories" 930:"Principles of Soft Computing, 3ed" 25: 28:Bidirectional associative memory 18:Bidirectional Associative Memory 849:{\displaystyle E(A,B)=-AMA^{T}} 762:{\displaystyle E(A,B)=-AMB^{T}} 50:in that they are both forms of 821: 809: 734: 722: 692: 666: 646: 634: 614: 602: 555: 543: 471: 459: 1: 698:{\displaystyle (A_{f},B_{f})} 71:A BAM contains two layers of 871:Self-organizing feature map 246:denotes the transpose. So, 1022: 1006:Artificial neural networks 962:. PHI Learning Pvt. Ltd. 477:{\displaystyle \min(m,n)} 239:{\displaystyle X_{i}^{T}} 160:From there, we calculate 627:. When a paired pattern 38:. BAM was introduced by 36:recurrent neural network 426:{\displaystyle M=\left} 866:Autoassociative memory 850: 789: 763: 699: 653: 621: 589: 562: 518: 498: 478: 427: 240: 208: 119: 92: 851: 790: 764: 700: 654: 652:{\displaystyle (A,B)} 622: 620:{\displaystyle (A,B)} 590: 563: 561:{\displaystyle (A,B)} 519: 499: 479: 428: 241: 209: 120: 118:{\displaystyle W^{T}} 93: 803: 773: 716: 663: 631: 599: 579: 540: 508: 488: 453: 252: 218: 164: 102: 82: 788:{\displaystyle A=B} 235: 192: 934:www.wileyindia.com 892:Kosko, B. (1988). 846: 785: 759: 705:is reached, which 695: 649: 617: 595:, with each state 585: 575:(energy function) 558: 514: 494: 474: 423: 417: 236: 221: 204: 178: 115: 88: 969:978-81-203-2186-1 588:{\displaystyle E} 573:Lyapunov function 517:{\displaystyle m} 497:{\displaystyle n} 91:{\displaystyle W} 16:(Redirected from 1013: 974: 973: 953: 944: 943: 941: 940: 926: 917: 916: 913:10.1109/21.87054 898: 889: 855: 853: 852: 847: 845: 844: 794: 792: 791: 786: 768: 766: 765: 760: 758: 757: 704: 702: 701: 696: 691: 690: 678: 677: 658: 656: 655: 650: 626: 624: 623: 618: 594: 592: 591: 586: 567: 565: 564: 559: 523: 521: 520: 515: 503: 501: 500: 495: 483: 481: 480: 475: 432: 430: 429: 424: 422: 418: 245: 243: 242: 237: 234: 229: 213: 211: 210: 205: 203: 202: 201: 191: 186: 124: 122: 121: 116: 114: 113: 97: 95: 94: 89: 48:Hopfield network 44:auto-associative 21: 1021: 1020: 1016: 1015: 1014: 1012: 1011: 1010: 996: 995: 982: 977: 970: 955: 954: 947: 938: 936: 928: 927: 920: 896: 891: 890: 883: 879: 862: 836: 801: 800: 795:corresponds to 771: 770: 749: 714: 713: 682: 669: 661: 660: 629: 628: 597: 596: 577: 576: 538: 537: 534: 506: 505: 486: 485: 451: 450: 447: 438: 416: 415: 410: 405: 400: 391: 390: 385: 377: 372: 366: 365: 360: 355: 350: 341: 340: 332: 327: 322: 316: 315: 310: 305: 297: 291: 290: 282: 277: 272: 261: 250: 249: 216: 215: 193: 162: 161: 136: 131: 105: 100: 99: 80: 79: 69: 63: 34:) is a type of 23: 22: 15: 12: 11: 5: 1019: 1017: 1009: 1008: 998: 997: 994: 993: 988: 981: 980:External links 978: 976: 975: 968: 945: 918: 880: 878: 875: 874: 873: 868: 861: 858: 843: 839: 835: 832: 829: 826: 823: 820: 817: 814: 811: 808: 784: 781: 778: 756: 752: 748: 745: 742: 739: 736: 733: 730: 727: 724: 721: 694: 689: 685: 681: 676: 672: 668: 648: 645: 642: 639: 636: 616: 613: 610: 607: 604: 584: 557: 554: 551: 548: 545: 533: 530: 513: 493: 473: 470: 467: 464: 461: 458: 446: 443: 437: 434: 421: 414: 411: 409: 406: 404: 401: 399: 396: 393: 392: 389: 386: 384: 381: 378: 376: 373: 371: 368: 367: 364: 361: 359: 356: 354: 351: 349: 346: 343: 342: 339: 336: 333: 331: 328: 326: 323: 321: 318: 317: 314: 311: 309: 306: 304: 301: 298: 296: 293: 292: 289: 286: 283: 281: 278: 276: 273: 271: 268: 267: 264: 260: 257: 233: 228: 224: 200: 196: 190: 185: 181: 175: 172: 169: 158: 157: 154: 147: 146: 143: 135: 132: 130: 127: 112: 108: 87: 68: 65: 24: 14: 13: 10: 9: 6: 4: 3: 2: 1018: 1007: 1004: 1003: 1001: 992: 989: 987: 984: 983: 979: 971: 965: 961: 960: 952: 950: 946: 935: 931: 925: 923: 919: 914: 910: 906: 902: 895: 888: 886: 882: 876: 872: 869: 867: 864: 863: 859: 857: 841: 837: 833: 830: 827: 824: 818: 815: 812: 806: 798: 782: 779: 776: 754: 750: 746: 743: 740: 737: 731: 728: 725: 719: 710: 708: 687: 683: 679: 674: 670: 643: 640: 637: 611: 608: 605: 582: 574: 569: 552: 549: 546: 531: 529: 525: 511: 491: 468: 465: 462: 444: 442: 435: 433: 419: 412: 407: 402: 397: 394: 387: 382: 379: 374: 369: 362: 357: 352: 347: 344: 337: 334: 329: 324: 319: 312: 307: 302: 299: 294: 287: 284: 279: 274: 269: 262: 258: 255: 247: 231: 226: 222: 198: 194: 188: 183: 179: 173: 170: 167: 155: 152: 151: 150: 144: 141: 140: 139: 133: 128: 126: 110: 106: 85: 76: 74: 66: 64: 61: 58: 56: 53: 49: 45: 41: 37: 33: 29: 19: 958: 937:. Retrieved 933: 907:(1): 49–60. 904: 900: 711: 570: 535: 526: 448: 439: 248: 159: 148: 137: 77: 70: 62: 59: 31: 27: 26: 52:associative 939:2020-08-15 877:References 40:Bart Kosko 828:− 741:− 532:Stability 484:, where " 395:− 380:− 345:− 335:− 300:− 285:− 174:∑ 129:Procedure 1000:Category 860:See also 799:. (i.e. 445:Capacity 134:Learning 67:Topology 536:A pair 73:neurons 966:  436:Recall 214:where 55:memory 897:(PDF) 707:Kosko 964:ISBN 909:doi 856:). 457:min 32:BAM 1002:: 948:^ 932:. 921:^ 905:18 903:. 899:. 884:^ 972:. 942:. 915:. 911:: 842:T 838:A 834:M 831:A 825:= 822:) 819:B 816:, 813:A 810:( 807:E 783:B 780:= 777:A 755:T 751:B 747:M 744:A 738:= 735:) 732:B 729:, 726:A 723:( 720:E 693:) 688:f 684:B 680:, 675:f 671:A 667:( 647:) 644:B 641:, 638:A 635:( 615:) 612:B 609:, 606:A 603:( 583:E 556:) 553:B 550:, 547:A 544:( 512:m 492:n 472:) 469:n 466:, 463:m 460:( 420:] 413:2 408:0 403:0 398:2 388:0 383:2 375:2 370:0 363:2 358:0 353:0 348:2 338:2 330:0 325:0 320:2 313:0 308:2 303:2 295:0 288:2 280:0 275:0 270:2 263:[ 259:= 256:M 232:T 227:i 223:X 199:i 195:Y 189:T 184:i 180:X 171:= 168:M 111:T 107:W 86:W 30:( 20:)

Index

Bidirectional Associative Memory
recurrent neural network
Bart Kosko
auto-associative
Hopfield network
associative
memory
neurons
Lyapunov function
Kosko
Hopfield's Auto-associative Energy Function
Autoassociative memory
Self-organizing feature map


"Bidirectional Associative Memories"
doi
10.1109/21.87054


"Principles of Soft Computing, 3ed"


NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHM: SYNTHESIS AND APPLICATIONS (WITH CD)
ISBN
978-81-203-2186-1
Bidirectional Associative Memory – Python source code for the Wiki article
Bidirectional associative memories – ACM Portal Reference
Category
Artificial neural networks

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