automatic generation of fuzzy rule base for online hre .ps

收集了遗传算法、进化计算、神经网络、模糊系统、人工生命、复杂适应系统等相关领域近期的参考论文和研究报告

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0.65 (Pub) 63.65 792.95 P
0.65 (lished in the Proceedings of the Second European Cong) 79.48 792.95 P
0.65 (ress on Intelligent T) 308.73 792.95 P
0.65 (ech-) 388.14 792.95 P
(niques and Soft Computing \050EUFIT\051, Aachen, pp) 63.65 779.95 T
(. 1060-1065, 1994.) 256.92 779.95 T
1 14 Q
(Automatic Generation of a Fuzzy Rule Base for Online) 99.83 744.62 T
(Handwriting Recognition) 206.23 724.62 T
2 12 Q
(Ashutosh Malaviya, Hartmut Surmann and Liliane Peters) 128.69 691.95 T
3 10 Q
(Ger) 147.7 674.29 T
(man National Resear) 165.07 674.29 T
(ch Center for Computer Science \050GMD\051) 265.79 674.29 T
(Schlo\247 Birlinghoven, 53754 St. Augustin, Ger) 175.66 662.29 T
(many) 392.43 662.29 T
([e-mail]) 229.75 650.29 T
130.05 593.29 72.65 593.29 2 L
V
0.5 H
0 Z
N
1 F
(ABSTRACT) 72.65 594.29 T
3.52 (An automatic method to generate fuzzy) 72.65 577.29 P
2.72 (rules and their membership functions to) 72.65 563.29 P
12.5 (r) 72.65 549.29 P
12.5 (ecognize handwritten characters is) 77.15 549.29 P
-0.27 (described. Firstly an initial rule base is cr) 72.65 535.29 P
-0.27 (e-) 288.25 535.29 P
1.35 (ated on the basis of a r) 72.65 521.29 P
1.35 (efer) 200.05 521.29 P
1.35 (ential data set) 219.95 521.29 P
0.96 (containing handwriting pr) 72.65 507.29 P
0.96 (ototypes. Subse-) 210.88 507.29 P
2 (quently the classi\336cation behavior of the) 72.65 493.29 P
3.87 (fuzzy rules is optimized with a genetic) 72.65 479.29 P
2.17 (algorithm, which is r) 72.65 465.29 P
2.17 (egar) 187.85 465.29 P
2.17 (ded as a typical) 209.75 465.29 P
1.46 (solution to NP-complete pr) 72.65 451.29 P
1.46 (oblems. A suit-) 217.13 451.29 P
0.58 (able \336tness function which corr) 72.65 437.29 P
0.58 (esponds to) 240.87 437.29 P
4.54 (the human per) 72.65 423.29 P
4.54 (ception of the linguistic) 158.43 423.29 P
3.46 (variables is obtained. The pr) 72.65 409.29 P
3.46 (oposed rule) 234.19 409.29 P
3.97 (generation pr) 72.65 395.29 P
3.97 (ocess extends the lear) 146.73 395.29 P
3.97 (ning) 274.65 395.29 P
1.16 (and adaptive capabilities of existing fuzzy) 72.65 381.29 P
(rule based r) 72.65 367.29 T
(ecognition system.) 133.55 367.29 T
1.73 (Keywor) 72.65 349.29 P
1.73 (ds:) 111.35 349.29 P
3 F
1.53 ( fuzzy featur) 126.35 349.29 P
1.53 (es, fuzzy rule genera-) 189.86 349.29 P
(tion, genetic algorithm) 72.65 335.29 T
1 F
(.) 178.25 335.29 T
1 12 Q
(1.0) 72.65 296.95 T
(INTRODUCTION) 99.65 296.95 T
2 10 Q
0.83 (Online character r) 72.65 276.29 P
0.83 (ecognition systems have to) 163.97 276.29 P
1.22 (operate in r) 72.65 262.29 P
1.22 (eal-time and have to cope with a) 131.75 262.29 P
4.77 (multiple number of users. These r) 72.65 248.29 P
4.77 (equir) 263.18 248.29 P
4.77 (e-) 288.45 248.29 P
(ments can only be ful\336lled if) 72.65 234.29 T
0.64 (i\051 number of character pr) 72.65 216.29 P
0.64 (ototypes is r) 198.49 216.29 P
0.64 (educed,) 259.65 216.29 P
(and simultaneously) 83.45 202.29 T
1.37 (ii\051 classi\336cation method is \337exible enough to) 72.65 184.29 P
4.98 (match various characters fr) 83.45 170.29 P
4.98 (om multiple) 234.07 170.29 P
(users.) 83.45 156.29 T
1.68 (The pr) 72.65 138.29 P
1.68 (oposed scheme achieve these r) 106.2 138.29 P
1.68 (equir) 263.18 138.29 P
1.68 (e-) 288.45 138.29 P
1.75 (ments with the \337exible and ef) 72.65 124.29 P
1.75 (\336cient genera-) 226.1 124.29 P
-0.09 (tion of a fuzzy rule base \050FRB\051. The pr) 72.65 110.29 P
-0.09 (ototypes) 256.85 110.29 P
1.3 (ar) 72.65 96.29 P
1.3 (e r) 82.92 96.29 P
1.3 (epr) 97.09 96.29 P
1.3 (esented by linguistic fuzzy rules and) 112.96 96.29 P
0.18 (the r) 306.65 594.29 P
0.18 (equir) 330.1 594.29 P
0.18 (ed \337exibility is obtained by the vari-) 355.37 594.29 P
0.43 (able width of the membership functions. Real) 306.65 580.29 P
1.75 (time classi\336cation of handwritten characters) 306.65 566.29 P
0.9 (is achieved by limited number of rules and a) 306.65 552.29 P
0.92 (fast pr) 306.65 538.29 P
0.92 (ocessing algorithm [9]. In this applica-) 339.45 538.29 P
6.43 (tion the syntactic r) 306.65 524.29 P
6.43 (elations between the) 418.6 524.29 P
3.17 (extracted featur) 306.65 510.29 P
3.17 (es in the for) 387.49 510.29 P
3.17 (m of linguistic) 455.71 510.29 P
0.2 (rules have been utilized to describe the hand-) 306.65 496.29 P
(written characters.) 306.65 482.29 T
0.56 (The paper is or) 306.65 464.29 P
0.56 (ganized as follows: in the next) 381.87 464.29 P
1.86 (section an outline of the fuzzy online hand-) 306.65 450.29 P
4.93 (writing r) 306.65 436.29 P
4.93 (ecognition system \050FOHRES\051[6] is) 353.25 436.29 P
2.23 (given. Section 3 handles with the de\336nition) 306.65 422.29 P
0.89 (and syntax of the fuzzy rule base. In the two) 306.65 408.29 P
0.3 (subsequent sections initialization and optimi-) 306.65 394.29 P
6.66 (zation of the fuzzy rule base \050FRB\051 is) 306.65 380.29 P
1.55 (explained. The last section pr) 306.65 366.29 P
1.55 (esents some of) 456.74 366.29 P
(the experimental r) 306.65 352.29 T
(esults.) 396.52 352.29 T
1 12 Q
(2.0) 306.65 313.95 T
(Outline of the FOHRES System) 333.65 313.95 T
2 10 Q
0.92 (The human visual system functions success-) 306.65 293.29 P
2.85 (fully even when patter) 306.65 279.29 P
2.85 (ns possess a certain) 424.1 279.29 P
5.98 (vagueness, slight mismatch and impr) 306.65 265.29 P
5.98 (eci-) 514.25 265.29 P
3.13 (sion[11]. It is able to select those featur) 306.65 251.29 P
3.13 (es) 521.25 251.29 P
1.22 (which identify the patter) 306.65 237.29 P
1.22 (n while ignoring the) 430.6 237.29 P
1.88 (r) 306.65 223.29 P
1.88 (est of the uncertainties. Based on this fact) 311.12 223.29 P
-0.31 (we have applied the theory of fuzzy logic to the) 306.65 209.29 P
5.33 (r) 306.65 195.29 P
5.33 (ecognition pr) 311.12 195.29 P
5.33 (ocess. Our solution for the) 380.33 195.29 P
1.81 (online handwriting r) 306.65 181.29 P
1.81 (ecognition pr) 410.34 181.29 P
1.81 (oblem esti-) 476.04 181.29 P
3.15 (mates the impr) 306.65 167.29 P
3.15 (ecision or the vagueness of) 387.43 167.29 P
2.32 (acquir) 306.65 153.29 P
2.32 (ed handwritten symbols in thr) 337.72 153.29 P
2.32 (ee pr) 495.45 153.29 P
2.32 (o-) 522.05 153.29 P
0.75 (cessing stages, pr) 306.65 139.29 P
0.75 (epr) 394.81 139.29 P
0.75 (ocessing, featur) 410.69 139.29 P
0.75 (e extrac-) 488.5 139.29 P
2.4 (tion and rule generation/classi\336cation [6][8]) 306.65 125.29 P
0.76 (and subsequently with suitable actions these) 306.65 111.29 P
3.13 (uncertainties ar) 306.65 97.29 P
3.13 (e r) 387.85 97.29 P
3.13 (educed or eradicated. In) 403.85 97.29 P
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(Fig 1. Outline of the FOHRES) 232.67 558.95 T
63.65 537.95 531.65 762.95 C
97.4 580.95 497.9 752.95 C
0 0 0 1 0 0 0 K
0 0 0 1 0 0 0 K
222.43 599.95 483.43 743.95 R
4 X
0 0 0 1 0 0 0 K
V
1 H
2 Z
0 X
N
1 14 Q
(FOHRES) 327.99 725.58 T
106.4 705.36 204.93 722.21 R
7 X
V
1 X
N
106.4 621.16 204.93 705.36 R
7 X
V
0.5 H
0 X
N
142.89 680.1 M
 106.4 680.1 106.4 680.1 124.64 671.68 D
 142.89 663.26 142.89 663.26 133.77 654.84 D
 124.64 646.42 124.64 646.42 161.14 646.42 D
7 X
V
1 H
0 X
N
183.03 654.84 M
 161.14 654.84 175.26 672.85 183.03 671.68 D
7 X
V
0 X
N
182.86 681.14 M
 172.08 686.2 191.46 688.53 190.8 681.47 D
 190.15 674.58 189.23 671.68 202.48 673.21 D
7 X
V
0 X
N
139.24 634.98 M
 128.29 634.98 135.35 643.4 139.24 642.85 D
7 X
V
0 X
N
1 12 Q
(=) 145.16 635.82 T
155.66 638.98 M
 161.12 631.95 161.14 646.42 155.74 639.44 D
7 X
V
0 X
N
107.49 688.53 118.44 691.48 118.44 685.57 118.44 691.48 4 L
7 X
V
0 X
N
255.02 612.74 225.46 587.48 284.57 536.95 314.14 562.22 4 Y
14 X
V
0 X
N
2 10 Q
(PENTOP) 135.07 710.45 T
143.78 690.68 M
 145.81 689.48 141.98 683.47 136.79 687.54 D
 133.99 689.74 139.55 693.58 143.57 690.68 D
7 X
V
0 X
N
133.61 691.02 M
 135.96 689.63 131.53 682.69 125.54 687.39 D
 122.3 689.93 128.72 694.36 133.37 691.02 D
7 X
V
0 X
N
196.98 637.08 204.67 630.51 2 L
7 X
V
3 X
N
211.76 612.13 M
 226.18 587.48 226.18 587.48 240.6 599.8 D
 255.02 612.13 255.02 612.13 226.18 624.45 D
 197.34 636.77 197.34 636.77 211.76 612.13 D
O
6 X
V
0 X
N
7 X
90 450 2.46 2.11 200.82 633.79 G
1 X
90 450 2.46 2.11 200.82 633.79 A
172.5 646.42 200.82 663.26 2 L
7 X
V
1 Z
0 X
N
186.89 646.51 M
 181.94 651.68 195.5 654.84 195.33 649.09 D
 194.99 638 210.27 642.54 194.99 638 D
7 X
V
0 X
N
240.43 655.08 341.68 718.69 R
7 X
V
0.5 H
2 Z
0 X
N
(FUZZY) 274.36 702.74 T
(-Pr) 255.1 684.23 T
(epr) 269.77 684.23 T
(ocessing) 285.65 684.23 T
(-Segmentation) 255.57 675.48 T
369.3 655.08 470.55 718.69 R
7 X
V
0 X
N
(FRB) 409.43 702.74 T
286.45 605.15 433.73 636.91 R
7 X
V
0 X
N
(CLASSIFICA) 317.2 615.77 T
(TION) 378.02 615.77 T
(-Generation) 389.24 684.56 T
356.29 682.34 358.01 685.32 368.39 682.34 358.01 679.37 4 Y
0 Z
N
356.29 682.34 358.01 685.32 368.39 682.34 358.01 679.37 4 Y
V
341.68 682.34 357.76 682.34 2 L
7 X
V
2 Z
0 X
N
396.91 648.76 399.89 647.04 396.91 636.66 393.93 647.04 4 Y
0 Z
N
396.91 648.76 399.89 647.04 396.91 636.66 393.93 647.04 4 Y
V
396.91 655.08 396.91 647.29 2 L
7 X
V
2 Z
0 X
N
227.42 680.95 229.14 683.93 239.52 680.95 229.14 677.98 4 Y
0 Z
N
227.42 680.95 229.14 683.93 239.52 680.95 229.14 677.98 4 Y
V
204.43 680.95 228.89 680.95 2 L
7 X
V
2 Z
0 X
N
304.86 649.91 307.84 648.19 304.86 637.82 301.89 648.19 4 Y
0 Z
N
304.86 649.91 307.84 648.19 304.86 637.82 301.89 648.19 4 Y
V
304.86 655.08 304.86 648.44 2 L
7 X
V
2 Z
0 X
N
(-Optimization) 386.44 675.48 T
(-Featur) 243.15 666.39 T
(e Extraction) 279.61 666.39 T
(-Adaptation) 391.14 666.39 T
415.32 642.08 412.35 643.8 415.32 654.18 418.3 643.8 4 Y
0 Z
N
415.32 642.08 412.35 643.8 415.32 654.18 418.3 643.8 4 Y
V
415.32 636.91 415.32 643.55 2 L
7 X
V
2 Z
0 X
N
(Diagnosis) 421.08 639.14 T
(\050Fuzzy RuleBase\051) 378.35 692.74 T
63.65 537.95 531.65 762.95 C
-8.35 24.95 603.65 816.95 C
2 10 Q
0 X
0 0 0 1 0 0 0 K
2.13 (the pr) 72.65 513.29 P
2.13 (epr) 104.25 513.29 P
2.13 (ocessing stage low level uncertain-) 120.13 513.29 P
0.98 (ties e.g. pentop err) 72.65 499.29 P
0.98 (ors, ar) 167.06 499.29 P
0.98 (e eliminated. In the) 199.91 499.29 P
5.75 (next stage these symbols ar) 72.65 485.29 P
5.75 (e segmented) 231.3 485.29 P
2.65 (accor) 72.65 471.29 P
2.65 (ding to the sudden changes and local) 98.92 471.29 P
6.03 (minima. Henceforth in the fuzzy featur) 72.65 457.29 P
6.03 (e) 292.45 457.29 P
2.6 (extraction stage, geometrical and other fea-) 72.65 443.29 P
1.45 (tur) 72.65 429.29 P
1.45 (es \050e.g. a) 87.72 429.29 P
3 F
1.36 (vertical line) 137.86 429.29 P
2 F
1.45 ( at) 194.21 429.29 P
3 F
1.36 (lef) 213.11 429.29 P
2 F
1.45 (t,) 224.71 429.29 P
3 F
1.36 (thin) 236.35 429.29 P
2 F
1.45 ( or) 254.95 429.29 P
3 F
1.36 (wide) 274.25 429.29 P
4.57 (symbol, C-Like or D-Like curves) 72.65 415.29 P
2 F
4.87 ([6]\051 of the) 241.91 415.29 P
1.57 (acquir) 72.65 401.29 P
1.57 (ed symbol ar) 103.72 401.29 P
1.57 (e deter) 169.74 401.29 P
1.57 (mined in the for) 204.82 401.29 P
1.57 (m) 288.25 401.29 P
3.68 (of fuzzy membership functions [12]. These) 72.65 387.29 P
4.96 (featur) 72.65 373.29 P
4.96 (es ar) 101.92 373.29 P
4.96 (e further pr) 130.74 373.29 P
4.96 (ocessed with fuzzy) 197.34 373.29 P
0.2 (operations \050max,) 72.65 359.29 P
4 F
0.16 (a) 157.85 359.29 P
2 F
0.2 (-cut etc.\051 to r) 164.16 359.29 P
0.2 (educe the fea-) 228.44 359.29 P
2.62 (tur) 72.65 345.29 P
2.62 (e set for computational ease in the rule) 87.72 345.29 P
1.55 (generation/classi\336cation stage. Scope of this) 72.65 331.29 P
-0.07 (paper is limited to the \336nal stage i.e. rule gen-) 72.65 317.29 P
1.78 (eration and classi\336cation fr) 72.65 303.29 P
1.78 (om the extracted) 211.88 303.29 P
(fuzzy featur) 72.65 289.29 T
(es.) 130.12 289.29 T
0.5 (In the classi\336cation stage vagueness exists in) 72.65 271.29 P
0.71 (the syntactical interpr) 72.65 257.29 P
0.71 (etation of the extracted) 182.53 257.29 P
1.22 (featur) 72.65 243.29 P
1.22 (es and fuzzy rules. This ambiguity can) 101.92 243.29 P
2.58 (be ter) 72.65 229.29 P
2.58 (med as syntactic vagueness. However) 103.54 229.29 P
-0.01 (this implies that the acquir) 72.65 215.29 P
-0.01 (ed fuzzy structural) 205.67 215.29 P
3.43 (elements have certain corr) 72.65 201.29 P
3.43 (espondence with) 212.62 201.29 P
0.53 (each other) 72.65 187.29 P
0.53 (. But this corr) 124.14 187.29 P
0.53 (espondence does not) 194.79 187.29 P
1.76 (always have unequivocal meanings and sec-) 72.65 173.29 P
1.84 (ondly it is dif) 72.65 159.29 P
1.84 (\336cult for a human to generate) 142.25 159.29 P
1.68 (enor) 72.65 145.29 P
1.68 (mous syntactic de\336nitions manually. T) 94.76 145.29 P
1.68 (o) 292.05 145.29 P
4.43 (over) 72.65 131.29 P
4.43 (come these syntactical ambiguities we) 93.12 131.29 P
0.67 (have employed the genetic algorithms to gen-) 72.65 117.29 P
-0.17 (erate and optimize fuzzy linguistic rules in the) 72.65 103.29 P
0 0 0 1 0 0 0 K
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0 0 0 1 0 0 0 K
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0 0 0 1 0 0 0 K
1.44 (for) 306.65 513.29 P
1.44 (m of a knowledge base or fuzzy rule base) 320.16 513.29 P
(\050FRB\051 automatically[10].) 306.65 499.29 T
2.27 (The FRB generation is accomplished in two) 306.65 481.29 P
1.9 (phases. In the \336rst phase a fuzzy rule base) 306.65 467.29 P
4.43 (\050FRB\051 is initialized with a combination of) 306.65 453.29 P
1.44 (expert knowledge and some stochastic infor-) 306.65 439.29 P
3.67 (mation. Afterwar) 306.65 425.29 P
3.67 (ds this knowledge base is) 392.38 425.29 P
-0.15 (optimized to i