Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

Recommendations

Info

designed the weights (W) of a single-layered feed-forward neural net by taking the product of input feeder (I) and the transpose of the output feeder (O) : T W = I o O , where "o" denotes the max-min composition operator. ∼ ∼ Once the weight matrix between the output and the input layer is formed, one / / can estimate an output vector o when the input vector I is given: ∼ ∼ / / o Wo I . ∼ = ∼ Analogously, the input vector by w o . / T / ∼ = I o ∼ / ∼ I can also be estimated, when / o is given Recently, Saha and Konar [22] designed a new methodology for estimation of inversion of fuzzy matrix, W. By using their definition, one can compute I' by w o / −1 / I = o , where W ∼ −1 is defined as W −1 o W → I, the '→' symbol represents 'tends to' and I is the identity matrix. There exist matrices for a (n × n) W matrix. The best inverse, which is defined as wbest −1 , is where 2 − 1 ∑ ∑ δ ij is minimum for [ δ ij] = [ ( w o best w ) − I] and i j is generally used for estimation of I' using It may be added that in very special cases matrix support Kosko's model [14]. 14.9 Fuzzy Neural Nets n n ∼ / / − 1 w I = o best ∼ − 1 T w = best w o ∼ , and thus the inverse Conventional sets include elements with membership values zero or one. However, for many real world problems, the membership value of an element .
in a set need not be always binary but may assume any value between zero and one. In fuzzy logic Max, Min, composition, t- norm, s- norm, and other operators are used along with fuzzy sets to handle real world reasoning problems. Fuzzy logic has been successfully used for modeling artificial neural nets, [19] where the input and output parameters of the neural net are the membership distributions. Among the classical fuzzy neural nets, the OR-AND neuron model of Pedrycz [19] needs special mention. In an OR-AND neuron, we have two OR nodes and one AND node [vide fig. 14.13]. Here, Z1 = ∧ ( Wij ∨ xj) 1 ≤ j≤ n Z2 = ∨ ( Vij ∧ xj) 1 ≤ j≤ n and y= (S11 ∧ Z1 ) ∨ (S12 ∧ Z2 ) where 'Λ' and 'V' denote fuzzy 't' and 's' norm operators. The above two operations are executed by using expressions (14.12) and (14.13). x1 Λ x2 = x1.x2 (14.12) x1 V x2 = (x1+x2−−−− x1.x2) (14.13) where '.' and '+' are typical algebraic multiplication and summation operations. Pedrycz devised a new algorithm for training an AND-OR neuron, when input membership distributions xi for i =1 to n and target scalar y are given. The training algorithm attempts to adjust the weights, wi j, so that a pre-defined performance index, is optimized. Pedrycz also designed a pseudo-median filter for using AND-OR neurons, which has many applications in median filtering under image processing. The typical organization of a pseudo-median filter for five delta points is given in fig.14.14.
Page 2 and 3:
Artificial Intelligence and Soft Co
Page 4 and 5:
Library of Congress Cataloging-in-P
Page 6 and 7:
understanding of the subject. The r
Page 8 and 9:
4. Structural organization of the b
Page 10 and 11:
ACKNOWLEDGMENT The author gratefull
Page 12 and 13:
To my parents, Mr. Sailen Konar and
Page 14 and 15:
Chapter 2: The Psychological Perspe
Page 16 and 17:
5.4.2 Syntactic Methods for Theorem
Page 18 and 19:
9.4.4 Realization of Fuzzy Inferenc
Page 20 and 21:
Chapter 14: Machine Learning Using
Page 22 and 23:
17.4.2.7 Fusing Multi-sensory Data
Page 24 and 25:
22.4 Parallelism at Knowledge Repre
Page 26 and 27:
1 Introduction to Artificial Intell
Page 28 and 29:
instances of decision making [27],
Page 30 and 31:
End. Begin state := new-states - ex
Page 32 and 33:
(a) Generate and Test Approach: Thi
Page 34 and 35:
this problem is useful for the foll
Page 36 and 37:
disciplines. As a young discipline
Page 38 and 39:
The bird and its attributes here ha
Page 40 and 41:
[19] , Kosko [15] and Pedrycz [30]
Page 42 and 43:
Reasoning in the presence of imprec
Page 44 and 45:
where AI finds extensive applicatio
Page 46 and 47:
M1 M2 M3 J2 J 1 8 5 J 2 J 1 2 8 J 2
Page 48 and 49:
1.5.3 The Modern Period The modern
Page 50 and 51:
A B AND C D E AND Y Fig. 1.12: A Pe
Page 52 and 53:
improve reliability by realizing fr
Page 54 and 55:
in AI problems, it supports massive
Page 56 and 57:
[7] Dean, T., Allen, J. and Aloimon
Page 58 and 59:
[34] Rajendra, C. and Chaudhury, D.
Page 60 and 61:
2 The Psychological Perspective of
Page 62 and 63:
The scope of realization of the pro
Page 64 and 65:
2.2.3 Feature-based Approach for Pa
Page 66 and 67:
From Sensory Registers Echoic Reg.
Page 68 and 69:
cognitive memory and its utilizatio
Page 70 and 71:
Tulving’s model bridges a potenti
Page 72 and 73:
A " (a) (b) Fig. 2.6: The character
Page 74 and 75:
his friend s facial image in memory
Page 76 and 77:
sum need not be 180 degrees. Thus a
Page 78 and 79:
same evening. Then he started medit
Page 80 and 81:
construction of knowledge and its o
Page 82 and 83:
saved in a magnetic media, which he
Page 84 and 85:
set-point + error Controller Plant
Page 86 and 87:
Cognitive science has emerged as a
Page 88 and 89:
Further show that the Euclidean lea
Page 90 and 91:
[4] Biswas, B., Mukherjee, A. K. an
Page 92 and 93:
[28] Peterson, M. A., Kihlstrom, J.
Page 94 and 95:
Production Systems, Logical Calculu
Page 96 and 97:
against the data items of the WM. I
Page 98 and 99:
Suppose the WM contains the data Bi
Page 100 and 101:
To demonstrate the working principl
Page 102 and 103:
i) Prefer rules for firing, where u
Page 104 and 105:
In fig. 3.3, the antecedent clauses
Page 106 and 107:
possible offspring states are gener
Page 108 and 109:
Backward Reasoning: The backward re
Page 110 and 111:
p q r s denotes joint premises t w
Page 112 and 113:
3.11.1 Isolation of Knowledge and C
Page 114 and 115:
state, as the predecessor states of
Page 116 and 117:
[3] Buchanan, B. G. and Shortliffe,
Page 118 and 119:
issues: first ‘what to search’
Page 120 and 121:
We will now present two typical alg
Page 122 and 123:
Further, the first state within the
Page 124 and 125:
n1 n2 n3 n8 (a) (b) (c) (d) (e) (f
Page 126 and 127:
The iterative deepening search thus
Page 128 and 129:
legal next states will also lie on
Page 130 and 131:
End. Then push those children into
Page 132 and 133:
Procedure Best-First-Search Begin 1
Page 134 and 135:
End. have multiple parents; Under t
Page 136 and 137:
node. Under this circumstance, the
Page 138 and 139:
6. Pn- Γ the set of paths from nod
Page 140 and 141:
Therefore, f * (n’) = g* (n’) +
Page 142 and 143:
0 +10 B 1+3 C +4 D +5 E F G 2+2 3+1
Page 144 and 145:
The above algorithm considers two d
Page 146 and 147:
Step 1: X (7) Step 2: Step 3: U X (
Page 148 and 149:
(iv) If all of the nodes connected
Page 150 and 151:
If the values are propagated up to
Page 152 and 153:
ii) the alpha value of any MAX node
Page 154 and 155:
αmin =2 βmax =1 C αmin =1 e(n) =
Page 156 and 157:
eductions. Constraint satisfaction
Page 158 and 159:
5 The Logic of Propositions and Pre
Page 160 and 161:
5.2 Formal Definitions The followin
Page 162 and 163:
Definition 5.7: A propositional for
Page 164 and 165:
5.4.1 Semantic Method for Theorem P
Page 166 and 167:
8. p \/ (q Λ ¬ q) ⇔ p 9. p Λ (
Page 168 and 169:
) AND, OR Removal: If the L.H.S. co
Page 170 and 171:
Since all the terminals of the tree
Page 172 and 173:
p v q ¬ q v r p v r ¬ p v s r v s
Page 174 and 175:
Let AS = {A1, A2,, …..,An} be the
Page 176 and 177:
4. If P is a WFF and X is not a qua
Page 178 and 179:
original expression: ( ¬ P11 ∨
Page 180 and 181:
S: = S ∪ {variable or constant /
Page 182 and 183:
¬ Loves (john, mary) ¬Child (X)
Page 184 and 185:
which implies that if q is true the
Page 186 and 187:
ii) C is a ground instance of C [9]
Page 188 and 189:
) p→ (q→ r) ⇔ (p Λ q) → r
Page 190 and 191:
[6] Leinweber, D., “Knowledge-bas
Page 192 and 193:
of a ‘classroom scene’ interpre
Page 194 and 195:
Sits-on (Y, bench, classhour), Pers
Page 196 and 197:
Sits-on (mita, bench, classhour). 3
Page 198 and 199:
Teacher (X) Sub-goal fails Person (
Page 200 and 201:
Definition 6.5: A definite goal is
Page 202 and 203:
Example 6.5, presented below, illus
Page 204 and 205:
6.6.1 Risk of Using CUT It is to be
Page 206 and 207:
In the SLD tree for the Taxpayer pr
Page 208 and 209:
6.9 Fixed Points in Non-Horn Clause
Page 210 and 211:
6.11 Conclusions The main advantage
Page 212 and 213:
preferred X50 >= 2, default X100
Page 214 and 215:
7.1 Introduction Predicate Logic is
Page 216 and 217:
proposed this logic and called it n
Page 218 and 219:
We now present a few modal properti
Page 220 and 221:
Expert Reasoning System New data Dy
Page 222 and 223:
PR5: ¬Visits (Wife, market, Monday
Page 224 and 225:
IN (n1) + IN (n2) + (n11) retracts
Page 226 and 227:
may hold (and not must hold). An ex
Page 228 and 229:
The resulting stable consequences t
Page 230 and 231:
In this example, we call ψ = Bird
Page 232 and 233:
important factor in non-monotonic r
Page 234 and 235:
a) Boy(X) ∧ ¬ L ¬ Likes (X, cho
Page 236 and 237:
8 Structured Approach to Knowledge
Page 238 and 239:
predicate boy (jim). This can be re
Page 240 and 241:
Consequently, we represent the abov
Page 242 and 243:
Is-a Is-a Is-a Protozoa Bacteria Is
Page 244 and 245:
Example 8.2: This example illustrat
Page 246 and 247:
A common question that may be raise
Page 248 and 249:
∴ q is true. 2) Defeasible deriva
Page 250 and 251:
Examination Hall Seat Arrangement I
Page 252 and 253:
Petri nets [5]. It, however, does n
Page 254 and 255:
Since p1 and p 2 contain tokens and
Page 256 and 257:
For building dependency structures,
Page 258 and 259:
The most significant drawback of CD
Page 260 and 261:
Once a script structure for a given
Page 262 and 263:
[6] Nilsson, N. J., Principles of A
Page 264 and 265:
techniques, we first introduce the
Page 266 and 267:
of precision of data and certainty
Page 268 and 269:
where D and S stand for disease and
Page 270 and 271:
To illustrate the reasoning process
Page 272 and 273:
2. It might happen that none of the
Page 274 and 275:
One interesting property of Bayesia
Page 276 and 277:
We now compute the messages that no
Page 278 and 279:
The main steps [8], [12] of the bel
Page 280 and 281:
Updating a node X thus involves upd
Page 282 and 283:
j ↓ i→ round oval-shaped and MB
Page 284 and 285:
Formally, the set of all possible o
Page 286 and 287:
The orthogonal summation of belief
Page 288 and 289:
The orthogonal summation operations
Page 290 and 291:
integers, then we can definitely sa
Page 292 and 293:
where a / b in fast-runner subset r
Page 294 and 295:
where the “o” denotes a fuzzy A
Page 296 and 297:
This may be formally written as µ
Page 298 and 299:
cumulative maximum of the two succe
Page 300 and 301:
“If you can manage to evaluate th
Page 302 and 303:
[7] Shenoy, P. P. and Shafer, G.,
Page 304 and 305:
10.1 Introduction Databases associa
Page 306 and 307:
a finite set of places, D= {d1, d2
Page 308 and 309:
µfast runner(x) 0.9 0.6 0.2 0.1 5
Page 310 and 311:
present in the consequent part; Aug
Page 312 and 313:
Procedure Cycle-detection (P, Q, m,
Page 314 and 315:
Procedure Put-transition (Newlistk,
Page 316 and 317:
The trace of the algorithm cycle- d
Page 318 and 319:
p1 p2 pm n1 n2 nm Fig. 10.4: A tran
Page 320 and 321:
where the elements qw / ∈ {0, 1}
Page 322 and 323:
Q' m and Rm matrices. It may be not
Page 324 and 325:
Definition 10.9: An arc is called p
Page 326 and 327:
The procedure forward reasoning is
Page 328 and 329:
In the above example, A, B, and C a
Page 330 and 331:
n V (q k j ∧r j k ) to be close t
Page 332 and 333:
Let us assume for the sake of simpl
Page 334 and 335:
It is evident from the above distri
Page 336 and 337:
= R b m o ((P ' f m) T o Nf c (t+1)
Page 338 and 339:
Since such choice of Rfm and Rbm sa
Page 340 and 341:
one step of belief revision in the
Page 342 and 343:
10.9 Conclusions The chapter presen
Page 344 and 345:
References [1] Bugarin, A. J. and B
Page 346 and 347:
[24] Looney, C. G., "Fuzzy Petri ne
Page 348 and 349:
11 Reasoning with Space and Time Th
Page 350 and 351:
The other one is an extension by ne
Page 352 and 353:
touches another, the common points
Page 354 and 355:
Procedure Move-optimal (R , Startin
Page 356 and 357:
at Jadavpur University verified thi
Page 358 and 359:
µ above(θ) =sin 2 θ , when -∏
Page 360 and 361:
c c a b q r a b (b) (a) d d p q r p
Page 362 and 363:
11.5 Temporal Reasoning by Situatio
Page 364 and 365:
Holds( now, rain-ceased) Axiom (3)
Page 366 and 367:
Interpretation I Interpretation II
Page 368 and 369:
Example 11.3: Prove that Ā(p) ≡
Page 370 and 371:
11.6.3 Some Elementary Proofs in PT
Page 372 and 373:
The second rule is generally called
Page 374 and 375:
11.9 Conclusions The chapter demons
Page 376 and 377:
12 Intelligent Planning This chapte
Page 378 and 379:
sets opposite to the TV. So they oc
Page 380 and 381:
where On (X, Y) means the object X
Page 382 and 383:
this, let us consider the last prob
Page 384 and 385:
(B), we generate the old state 3, w
Page 386 and 387:
12.3 Least Commitment Planning The
Page 388 and 389:
To start solving the problem, we fi
Page 390 and 391:
We now have three partially ordered
Page 392 and 393:
12.4 Hierarchical Task Network Plan
Page 394 and 395:
d=0 d=1 d=2 d=3 Fig. 12.16: A hiera
Page 396 and 397:
J1 J2 M1 M2 M3 or Job schedules J2
Page 398 and 399:
job k from time 0. For example, the
Page 400 and 401:
3. Design a hierarchical plan for t
Page 402 and 403:
trainer, who supplies the input-out
Page 404 and 405: Definition 13.1: An object is an en
Page 406 and 407: The restriction of hypotheses can b
Page 408 and 409: Positive instances: 1. Object (larg
Page 410 and 411: To illustrate how LEX performs inte
Page 412 and 413: alive dead Z=A/W 2 1 0 0 1 2 Living
Page 414 and 415: where Pi denotes the proportion of
Page 416 and 417: 3. Apply Mapping Function: Apply th
Page 418 and 419: determine the animals because it do
Page 420 and 421: simplest form of reinforcement lear
Page 422 and 423: Let us now assume that the game is
Page 424 and 425: How can we compute the utility valu
Page 426 and 427: 13.5 Learning by Inductive Logic Pr
Page 428 and 429: know f ? These questions can be ans
Page 430 and 431: |H| (1-ε) m ≤ δ ⇒ m ≥ (1/ε
Page 432 and 433: [7] Michalski, R. S., “A theory a
Page 434 and 435: However, most biologists are of the
Page 436 and 437: The most common non-linear function
Page 438 and 439: (d) b1 a1 (e) c1 cj cq bi ah Fig.14
Page 440 and 441: transverse diameter, etc. as the in
Page 442 and 443: x 1 x 2 x n Penultimate layer ∑+F
Page 444 and 445: Drawbacks of back-propagation algor
Page 446 and 447: x1 x2 xn 14.6 Widrow-Hoff's Multi-l
Page 448 and 449: x 1 x 2 x 1 x 2 The training proces
Page 450 and 451: • If not, select another ADALINE,
Page 452 and 453: ni 1 = '1' output ( firing ). Each
Page 456 and 457: AND z1 s11 OR w11 w12 w 1n v 11 v12
Page 458 and 459: − ~ w X k, l ∼ = ⎡ ⎤ ⎢
Page 460 and 461: V x ∑ w O j = Sgn ( j+ ji i + A
Page 462 and 463: neural nets in the fields mentioned
Page 464 and 465: where the first summation is over a
Page 466 and 467: circuit,” IEEE Trans. on Circuits
Page 468 and 469: their joint usage in many problems,
Page 470 and 471: 1 0 1 0 11 0 1 0 1 0 1 1 0 10 1 1 1
Page 472 and 473: 15.2 Deterministic Explanation of H
Page 474 and 475: Thus, in a total of N selections wi
Page 476 and 477: and population size = 2, which mean
Page 478 and 479: The behavior of GA without mutation
Page 480 and 481: a = ∑ Lt (π 0 P n ) i i=1 n→
Page 482 and 483: Eθ GA Program Z1, Z2, Z3, Z4, X1,
Page 484 and 485: e [W1 W2 W3 W4 W5 W6] T and [ G1 G2
Page 486 and 487: function. So, GA in each evolution
Page 488 and 489: f(x) = Sin (x) + [x * x + y] 1/2 ca
Page 490 and 491: (EQ (DU(MT CS) (NOT CS)) (DU(MS NN)
Page 492 and 493: [4] Chakraborty, U. K. and Muehlenb
Page 494 and 495: 16 Realizing Cognition Using Fuzzy
Page 496 and 497: Cognitive maps are generally used f
Page 498 and 499: Theorem 16.1: With initial values o
Page 500 and 501: Here W T is a transposed weight mat
Page 502 and 503: example, for moving one’s arm, th
Page 504 and 505:
and the weight adaptation equation
Page 506 and 507:
Xk+1 ∆Wk = α Ek οXk+1 T / ((Xk+
Page 508 and 509:
d5 d9 d1 d6 d7 d8 d10 d11 d1=Front
Page 510 and 511:
16.10 Conclusions and Future Direct
Page 512 and 513:
[6] Kosko, B., “Fuzzy cognitive m
Page 514 and 515:
however, have a narrow spectrum of
Page 516 and 517:
contaminated with it. However, for
Page 518 and 519:
1. Move the mask over the image, so
Page 520 and 521:
∇ 2 (g * f ) = ( ∇ 2 g ) * f. W
Page 522 and 523:
It may be noted that segmenting an
Page 524 and 525:
machine has to narrate the game onl
Page 526 and 527:
schematic diagram, briefly outlinin
Page 528 and 529:
same process for all clusters, we g
Page 530 and 531:
Level 1 Level 2 Level 0 Neuron Fig.
Page 532 and 533:
I (u, v) YI Fig. 17.12: Camera mode
Page 534 and 535:
epipolar planes meet the image plan
Page 536 and 537:
(a, b, 1) Fig. 17.15: A 3-D represe
Page 538 and 539:
case III: Planes not parallel to th
Page 540 and 541:
yi = -fi (xi,* ai - 1*) + ( fi / a
Page 542 and 543:
Sample Execution: This is a program
Page 544 and 545:
U t11 t12 t13 t14 x V = t21 t22 t23
Page 546 and 547:
obtained after linearizing measurem
Page 548 and 549:
co-ordinate systems. The correspond
Page 550 and 551:
18 Linguistic Perception Building p
Page 552 and 553:
psychological aspects of understand
Page 554 and 555:
S NP VP ART N VP the man VP V NP ki
Page 556 and 557:
( S ( NP ( ( ART the ) (N man ) ) (
Page 558 and 559:
Sentence: NP VP 1 6 NP: ART N 2 ART
Page 560 and 561:
18.2.3.1 Learning For adaptation of
Page 562 and 563:
context sensitive grammar is thus m
Page 564 and 565:
Generally, we start the sentence S
Page 566 and 567:
VP: V NP S0 S1 Tagged procedure V:=
Page 568 and 569:
Laugh agent animal enjoyer entity p
Page 570 and 571:
18.5 Discourse and Pragmatic Analys
Page 572 and 573:
Thus the elements of communicative
Page 574 and 575:
19 Problem Solving by Constraint Sa
Page 576 and 577:
The recognition problem is the pres
Page 578 and 579:
CSP deals with two classical proble
Page 580 and 581:
= 10 10 = 10 = * Fig. 19.2 (b): Pro
Page 582 and 583:
+ _ V1 R1 V 2 V3 I1 I2 I3 V R2 R3 S
Page 584 and 585:
V = Now assuming the values of V, R
Page 586 and 587:
On (A, Ta) ∧ On (B, Ta) ∧On (C,
Page 588 and 589:
We now represent the neighboring re
Page 590 and 591:
Step 1: (x
Page 592 and 593:
such variable xj , xk, etc., attach
Page 594 and 595:
of C3. If the constraints are liste
Page 596 and 597:
The possible labels of the junction
Page 598 and 599:
The main part of CSP is the formula
Page 600 and 601:
20 Acquisition of Knowledge Acquisi
Page 602 and 603:
invite the experts to attend a meet
Page 604 and 605:
B E A= test ?, B = test results, C
Page 606 and 607:
The database in fig. 20.2 is extrac
Page 608 and 609:
20.5 Knowledge Refinement by Hebbia
Page 610 and 611:
* * * n i ( t + 1) = n i ( t ) at t
Page 612 and 613:
L(r,s) L(s,r) Y(r) Y(s) OS(r,s) n1
Page 614 and 615:
The FPN of fig. 20.5 is formed with
Page 616 and 617:
L(a,l) L(l,a) Y(a) Y(l) OS(a,l) n1
Page 618 and 619:
[3] Cooke, N. and Macdonald, J.,
Page 620 and 621:
21 Validation, Verification and Mai
Page 622 and 623:
Here the problem characteristic is
Page 624 and 625:
The chapter will cover various issu
Page 626 and 627:
21.2.1 Qualitative Methods for Perf
Page 628 and 629:
Paired t-test: Suppose xi ∈ X and
Page 630 and 631:
Ancestor P = {p1,p2} Tr = {tr1,tr2,
Page 632 and 633:
21.3.2 Inconsistencies in Knowledge
Page 634 and 635:
q tr1 tr2 p1 p2 …………… pn
Page 636 and 637:
A rule may also include a number of
Page 638 and 639:
21.4 Maintenance of Knowledge Based
Page 640 and 641:
control of data and knowledge is un
Page 642 and 643:
8. Stefik, M., Introduction to Know
Page 644 and 645:
are not suitable for AI application
Page 646 and 647:
Because of non-determinism in the s
Page 648 and 649:
Procedure Partial-Expansion Begin W
Page 650 and 651:
Step: Fig.22.4: A tree expanded by
Page 652 and 653:
Procedure Generous-Sharing Begin Fl
Page 654 and 655:
To evaluate the performance of the
Page 656 and 657:
Definition 22.2: If the antecedents
Page 658 and 659:
a) AND -Parallelism Consider a logi
Page 660 and 661:
However, given sufficient computing
Page 662 and 663:
EPN = { P, Tr, D, f, m, A, a, I, o
Page 664 and 665:
It is to be noted that if- then ope
Page 666 and 667:
After the bindings of the variables
Page 668 and 669:
F(x) ← A(X) , B(Y) (1) A(1) ← (
Page 670 and 671:
the result produced by clause (2) m
Page 672 and 673:
Else Flag:= true; End; Until no-of
Page 674 and 675:
transition for the PTVVM. The PTVVM
Page 676 and 677:
For the sake of convenience, the pi
Page 678 and 679:
22.6 Conclusions The chapter starte
Page 680 and 681:
Rule 3: S(X, Y) → Z (Y, X) Rule 4
Page 682 and 683:
23 Case Study I: Building a System
Page 684 and 685:
features in human faces and their r
Page 686 and 687:
Definition 23.4: A linear edge segm
Page 688 and 689:
lock containing edge. The productio
Page 690 and 691:
Definition 23.13: The measure of di
Page 692 and 693:
Partitioning Fe into Blocks Feature
Page 694 and 695:
The fuzzy membership functions used
Page 696 and 697:
∆ • Fig. 23.4: The delta and th
Page 698 and 699:
Thus we have altogether 6 different
Page 700 and 701:
cross-section of the vocal tract, w
Page 702 and 703:
23.4.2 Training a Multi-layered Neu
Page 704 and 705:
information with their normalized v
Page 706 and 707:
SR (Y, X) OR (M (X) ∧ M (Y) ∧ L
Page 708 and 709:
procedure, however, no attempts hav
Page 710 and 711:
23.5.5 Belief Revision and Limitcyc
Page 712 and 713:
that the inferences will be minimal
Page 714 and 715:
the formation of the FPN. The FPN o
Page 716 and 717:
is then initiated for detecting the
Page 718 and 719:
initial fuzzy beliefs at all places
Page 720 and 721:
at all feasible x, y. Then estimate
Page 722 and 723:
[13] Malmberg, B., Manual of Phonet
Page 724 and 725:
24.1 Mobile Robots Mobile robots ar
Page 726 and 727:
about its world. There exists ample
Page 728 and 729:
the process, else it attempts to fi
Page 730 and 731:
Depending on the type of planning p
Page 732 and 733:
WEST NORTH SOUTH Fig. 24.5: Represe
Page 734 and 735:
NW child B NE child 'C' SW child 'D
Page 736 and 737:
As observed from the node status, t
Page 738 and 739:
In their evolutionary planner algor
Page 740 and 741:
tuning of the path, such as avoidin
Page 742 and 743:
S S= starting point, G= Goal point,
Page 744 and 745:
Once the training is over, the syst
Page 746 and 747:
N o Table 24.2: Training pattern sa
Page 748 and 749:
24.9.1 Finite State Machine Finite
Page 750 and 751:
occurrence of a state, but also in
Page 752 and 753:
24.10 An Application in a Soccer Pl
Page 754 and 755:
Exercises 1. Devise an algorithm fo
Page 756 and 757:
[15] Meng, H. and Picton, P. D.,
Page 758 and 759:
24 + The Expectations from the Read
Page 760 and 761:
Appendix A How to Run the Sample Pr
Page 762 and 763:
Predicate: LSR Argument1: S Argumen
Page 764 and 765:
ANTECEDENT 1: Predicate: END conclu
Page 766 and 767:
Rules properly arranged MAIN MENU:
Page 768 and 769:
Level POSITION_FROM_LEFT NODE 1 1 H
Page 770 and 771:
calculating membership values enter
Page 772 and 773:
C > File currently in progress = ci
Page 774 and 775:
When the goal is within concave obs
Page 776 and 777:
path selected 160,160 and 240,240 T
Page 778 and 779:
(a) Robot entering the room (b) Aft
Page 780 and 781:
where tr and Outr denote the target
Page 782 and 783:
∂E/∂Wsp = ∑r (∂E/∂Outr) (
Page 784 and 785:
Proof of theorem10.2: For an oscill
Page 786 and 787:
Thus, we get analogously , and 1 0
Page 788:
= Q 'f m T o Rf m o ( Q ' f m o I )
show all

Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?