C++ Neural Networks and Fuzzy Logic
J Jagota, 514 January effect, 380 Jurik, 381, 384, 392 K
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C neural networks and fuzzy logic
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J Jagota, 514 January effect, 380 Jurik, 381, 384, 392 K Karhunen−Loev transform, 384 Katz, 377 Kimoto, 408 kohonen.dat file, 275, 298, 300, 317 Kohonen, 19, 116, 117, 245, 271, 303, 456 Kohonen feature map, 16, 271, 273, 303, 305, 323 conscience factor, 302 neighborhood size, 280, 299, 300 training law, 273 Kohonen layer, 9, 19, 82, 92, 106, 298, 302, 322 class, 276 Kohonen network, 275, 276, 280, 300, 303, 322 applications of, 302, Kohonen output layer, 275 Kohonen Self−Organizing Map, 115, 456, 471, 472 Kosaka, 409, Kosko, 49, 50, 104, 215, 242, 506 Kostenius, 408, 409 C++ Neural Networks and Fuzzy Logic:Preface Index 440
Kronecker delta function 428, 524 L lambda, 136, 433 late binding, 24 lateral, 93 lateral competition, 303 laterally connected, 65 lateral connections, 93, 97, 107, 272, 276 lateral inhibition, 272, 276 layer, 2, 81 C layer, 106 comparison, 244 complex layer, 106 F1, 244 F2, 244
Grossberg layer, 82, 92, 302 hidden layer, 75, 81, 86, 89 input layer, 2, 3, 82 Kohonen layer, 82, 92, 302, 322 middle layer, 329, 372 output layer, 2, 82 recognition, 244 S layer, 106 simple layer, 106 layout, 52, 86, 124 ART1, 244 BAM , 180 Brain−State−in−a−Box, 105 FAM, 219
Hopfield network, 11 for TSP, 427 LVQ, 117 Madaline model, 103 LBS Capital Management, 377 learning, 4, 74, 98, 109, 110, 117, 118 algorithm, 61, 79, 102, 118 cycle, 103 Hebbian, 105, 110 one−shot, 117 probabilistic, 113 rate(parameter), 111, 112, 123, 125, 127, 136, 175 supervised learning, 5, 110, 112, 117, 121 time, 120 unsupervised competitive learning, 271 learning, 5, 110, 117, 121 Learning Vector Quantizer, 115−117, 302 least mean squared error, 111, 119, 123, 419 C++ Neural Networks and Fuzzy Logic:Preface Index 441
rule, 111 Le Cun, 375 Lee, 512 Levenberg−Marquardt optimization, 373 Lewis, 377 Lin, 512
linear function, 99, 102 linear possibility regression model, 493, 496, 509 linear programming, 417 integer, 417 linearly separable, 83− 85 LMS see least mean squared error rule local minimum, 113, 177, 325 logic
boolean logic, 50 fuzzy logic, 31, 34, 50, 473 logical operations, 31 AND, 64
logistic function, 86, 100 Long−term memory, 6, 77− 79, 118, 472 traces of, 243 look−up
memory, 5 table, 106 LTM see Long−term memory LVQ see Learning Vector Quantizer Lyapunov Function, 118, 119
MACD see moving average convergence divergence Madaline, 102, 103 main diagonal, 63, 480 malignant melanoma, 514 malloc, 24 Mandelman, 378 MAPE see mean absolute percentage error mapping, 123, 180 binary to bipolar, 62, 63 inverse, 62, 182 nonlinear, 109 real to binary, 180 mapping surface, 109 Markowitz, 470 Marquez, 406 Mason, 516 matrix, 97, 521 addition, 521 correlation matrix, 9 fuzzy, 217 C++ Neural Networks and Fuzzy Logic:Preface Index 442
multiplication, 11 product, 104, 522 transpose, 11 weight matrix, 97 max_cycles, 326 maximum, 33, 219 max−min composition, 220, 221 McClelland, 103 McCulloch, 6 McNeill, 508 mean absolute percentage error, 406 mean squared error, 111, 410 Mears, 212, 214 membership, 32 functions, 50 triangular, 499, 506 rules, 49 memorization, 121, 320, 336, 382, 397 memorizing inputs, 273, 320 memory, 98 adaptive, 471 fuzzy associative, 218, 221 long−term memory, 6, 77−79, 118, 472 recency based, 471 short−term memory, 6, 77, 78, 79, 107, 118, 471 Mendelsohn, 407, 408 methods, 22, 145 metric, 5, 103 mexican hat function, 272, 273, 274, 276 middle layer, 329, 372 choosing size 372 minimum, 33, 219 global, 113, 177 local, 113, 177, 325 Minsky, 83 model
ART1, 245 continuous, 98 discrete, 98 Perceptron model, 65, 68, 81, 83 modulo, 423 Mohamed, 513 momentum, 325, 330, 337, 372, 400 implementing, 331 parameter, 119, 123, 134, 384 term, 330, 375 Morse, 514 Moss, 514 moving average convergence divergence, 401 moving averages, 380, 399 simple, 399 C++ Neural Networks and Fuzzy Logic:Preface Index 443
weighted, 399 multilayered, 92 network, 106 multilayer feed−forward neural network, 7 multilayer networks, 123 multiple inheritance, 26 Munro, 374
NF see noise factor NP−complete, 419, 427, 457 NYSE see New York Stock Exchange Naim, 457 neighborhood, 276, 303, 457 size, 274, 280, 299, 300 neighbors, 272 Neocognitron, 81, 92, 106 Nellis, 516 NETTalk, 374 network
adaptive, 77 architecture, 77, 384, 388 autoassociative network, 97 backpropagation network, 329 bi−directional associative memory network, 104,, 88 Brain−State−in−a−Box network, 105 class, 53, 66 heteroassociative networks, 97 Hopfield network, 9, 11−14, 16, 19, 51, 79, 81, 82, 93, 111, 119, 120, 181, 472 layout, 86 modeling, 73 multilayer network, 123 nodes, 65 Perceptron network, 65, 66, 68, 79 radial basis function networks, 112, 114, 115 RBF networks see radial basis function networks self−organizing , 269 statistical trained networks, 112 NeuFuz, 49 neural network, 1, 2 algorithms, 176 artificial neural network, 1 autoassociative, 375 biological, 272 counterpropagation, 302 fault tolerance of, 374 FAM, 218 hierarchical, 407 holographic, 408 C++ Neural Networks and Fuzzy Logic:Preface Index 444
Kohonen, 271, 322 multilayer, 123 Perceptron, 65 plug−in boards, 325 self−organizing, 107, 269 Tabu, 471 two−layer, 92 neural−trained fuzzy systems, 49 neuromimes 2 neuron, 1, 3 input neurons, 82 output neuron, 99 new, 24, 144 Newton’s method, 373 New York Stock Exchange, 387 new highs, 389 new lows, 389 noise, 5, 330, 336, 337, 372, 375 random, 375 noise factor, 336 noise−saturation dilemma, 79 noise tolerance, 105 noisy data, 320 nonlinear function, 120 nonlinear mapping, 109 nonlinear scaling function, 10 nonlinear optimization, 417, 422, 472 nontraining mode, 135 Normal distribution, 524 normal fuzzy set, 218 normalization of a vector, 272 normalized inputs, 271, 381 normalized weights, 280 notation, 132 nprm parameter, 433 number bins, 43 NYSE see New York Stock Exchange
object, 22 objective function, 417 object−orientation, 21 object−oriented programming language, 21 Object−Oriented Analysis and Design, 21 on−balance volume, 402 on center, off surround, 97, 98 one−shot learning, 117 oneuron class, 66 Operations Research, 34 C++ Neural Networks and Fuzzy Logic:Preface Index 445
operator overloading, 25, 139 optical character recognition, 245 recognizer, 514 optimization, 102, 109, 417 nonlinear, 417, 422, 472 stock portfolio, 470 ordered pair, 32 organization of layers for backpropagation program, 144 orienting subsystem, 107, 243 orthogonal, 11, 12, 51, 64, 98 bit patterns, 64 input vectors, 299 set, 65
ostream, 58 output, 99 layer, 2, 10 nature of , 74 number of , 74 space, 124 stream, 58 outstar, 93, 106 overfitting of data, 383 overlap composition method, 506 overloading, 21, 24 function overloading, 25, 139 operator overloading, 25, 139 overtrained network, 329 P Papert, 83 Parker, 103 partitioning, 87, 88 past_deltas, 331 Patil, 406 pattern association, 16 binary pattern, 11, 97 bit pattern, 99 character, 17 classification, 8, 98, 109 completion, 105, 109 matching, 8, 98 recognition, 16, 34, 102, 108, 305, 322 studies, 380 system, 121 spatial, 99, 214 Pavlovian, 5 Perceptron, 3, 4, 66, 73, 82, 87, 90, 93, 102, 112 C++ Neural Networks and Fuzzy Logic:Preface Index
446 model, 65, 68, 81, 83 multilayer Perceptron, 85, 88 network, 65, 66, 68, 79 single−layer Perceptron, 85 permutations, 420 Perry, 484 perturbation , 5, 113 phoneme, 303, 374 phonetic typewriter, 303 Pimmel, 513 Pitts, 6 pixel, 16, 322, 329, 374 values, 214, 305 plastic, 107 plasticity, 78, 79 plasticity−stability dilemma, 243 Pletta, 515 polymorphic function, 24, 28 polymorphism, 21, 24, 27, 138 Pomerleau, 374 portfolio selection, 470, 472 possibility distributions, 486, 487, 509 relational model, 486, 487 postprocess, 35 postprocessing, 50 filter , 50 potlpair class in BAM network, 186 preprocess, 35, 50 preprocessing , 87, 379, 399 data, 389 filter, 50 fuzzifier, 35 Price is Right, 3, 65 principal component analysis, 384 private, 23, 26 probabilities, 31, 419 probability, 31, 43 distributions, 113 processing additive, 75 hybrid, 75 multiplicative, 75 PROJECT operation, 485 proposition, 31 protected, 23, 26, 54, 143 public, 23, 26, 53, 54
C++ Neural Networks and Fuzzy Logic:Preface Index 447
quadratic form, 119, 120, 418 quadratic programming problem, 418 quantification, 473, 475 quantization levels, 322 queries, 475, 476, 488 fuzzy, 483, 488 R radial basis function networks, 112, 114, 115 ramp function, 99, 101,, 102 Ramsay, 515 random number generator, 37 range, 394 normalized, 394, 395 rate of change, 392, 400, 404 function, 392 indicator, 393, 394 real−time recurrent learning algorithm, 515 recall, 7, 81, 184, 220 BAM , 184 FAM, 220, 221 recency based memory, 471 recognition layer, 244 recurrent, 13, 179 recurrent connections, 82, 107, 179 reference activation level, 456 function, 493 regression analysis, 406 risk−adjusted return, 410 relations, 476 antisymmetric, 480, 481 reflexive, 480, 481 resemblance, 509 similarity, 481, 509 symmetric, 480, 481 transitive, 480, 481 relative strength index, 400, 404 remainder, 423 reset, 243, 247, 251, 262 reset node, 244 reset unit, 244 resonance, 104, 107, 117, 118, 181, 215, 243, 269 responsive exploration, 471 Ressler, 512 restrmax function, 251 return type, 23 reuse, 26 Robotics, 34 C++ Neural Networks and Fuzzy Logic:Preface Index
448 ROC see rate of change Rosenberg, 374 Ross, 517 row vector, 97, 104 RSI see relative strength index rule
delta, 110, 111 generalized delta, 112 Hebbian, 111 Hebb’s, 110 rules fuzzy rules, 50 Rumbaugh, 21 Rummelhart, 103 S SP500 Index see Standard and Poor’s 500 Index Sathyanarayan Rao, 515 saturate, 381 scalar, 61 scalar multiplier, 64 second derivative, 380 Sejnowski, 114, 374 self−adaptation, 115 self−connected, 53 self−organization, 5, 6, 74, 115, 116 self−organizing feature map, 116 Self−Organizing Map, 245, 271 self−organizing neural networks, 107, 117, 121 self−supervised backpropagation, 375 sensitivity analysis, 384 separable, 84, 86, 88 linearly separable, 83, 84 subsets, 87 separability, 84, 86 separating line, 86
plane, 85 Sethuraman, 515 set membership, 32 Sharda, 406 shift operator, 25 Short Term Memory, 6, 77, 78, 79, 107, 118, 471 traces of, 243 Sigma Pi neural network , 75 sigmoid activation function, 381, 387 function, 77, 99, 126, 129, 133, 164, 177, 395 squashing, 381 C++ Neural Networks and Fuzzy Logic:Preface Index
449 signal filtering, 102 signals
analog, 98 similarity, 486 class, 481, 509 level, 486 relation, 481 simple cells, 106 simple moving average, 399 simulated annealing, 113, 114 simulator, 372, 396 controls, 173 mode, 138 Skapura, 246, 248 Slater, 515 S layer, 106 SMA see simple moving average SOM see Self−Organizing Map sonar target recognition, 374 spatial
pattern, 99, 214 temporal pattern, 105, speech recognition, 303, synthesizer, 374, spike, 380 squared error, 103 squashing function, 384, 458, 459 stable, 79, 107 stability 78, 79, 118 and plasticity, 77 stability−plasticity dilemma, 79, 107,, 269 STM see Short Term Memory Standard and Poor’s 500 Index, 377, 378 forecasting, 386 standard I/O routines, 519 state energy, 118 state machine, 48 static binding, 139 Steele, 514 steepest descent, 112, 113, 177, 373 step function, 99, 101 stochastics, 402, 404 Stoecker, 514 Stonham, 516 string binary, 62 bipolar, 62 structure, 7, 7 subsample, 322 C++ Neural Networks and Fuzzy Logic:Preface Index 450
subset, 221 subsystem attentional, 107, 243 orienting, 107, 243 Sudjianto, 516 summand, 422 summation symbol, 422 supervised , 109 learning, 5, 110, 112, 115, 117, 121 training 94, 110, 115, 121, 125 Sweeney , 516 symbolic approach, 6 T TSR see Terminate and Stay Resident Tabu , 471 active, 471 neural network, 471 search, 471, 472 Tank, 422, 427, 429 target 378, 395 outputs, 110, 115 patterns, 105 scaled, 395 tau, 433
technical analysis, 399 temperature, 118 Temporal Associative Memory, 92 Terano, 496 Terminate and Stay Resident programs, 519 terminating value, 298 termination criterion, 322 test.dat file, 327, 328 test mode, 135, 137, 138, 164, 173, 327, 396 Thirty−year Treasury Bond Rate, 387 Three−month Treasury Bill Rate, 387 threshold function, 2, 3, 12, 17, 19, 52, 95, 99, 101, 125, 183 value, 16, 52, 66, 77, 86, 87, 90, 101, 128, 456 thresholding, 87, 185 function, 133, 177, 182, 184, 214 Thro, 508 Tic−Tac−Toe, 76, 79 time lag, 380 time series forecasting, 406, 410 time shifting, 395 timeframe, 378 tolerance, 119, 125, 173, 245, 318, 322, 328, 329, 372 level, 78, 123 C++ Neural Networks and Fuzzy Logic:Preface Index
451 value, 119 top−down
connection weight , 248 connections, 107, 244 top−down inputs, 247 topology, 7 Topology Preserving Maps, 116 tour, 420 traces, 243 of STM, 243 of LTM, 243 trading
commodities, 405, system, 378 dual confirmation, 408 training, 4, 74, 75, 98, 109, 110, 119, 181, 396 fast, 107 law, 272, 273, 274, 330, 333 mode, 135, 137, 138, 164, 173, 396 supervised, 94, 110, 115 slow, 107 time, 329 unsupervised, 107, 110 transpose of a matrix, 11, 179, 181, 183 of a vector, 11, 63, 97, 181 traveling salesperson(salesman) problem, 118, 119, 419 hand calculation, 423 Hopfield network solution−Hopfield, 427 Hopfield network solution−Anzai, 456 Kohonen network solution, 456 triple, 217 truth value, 31 tsneuron class, 430 TS see Tabu search TSP see traveling salesperson problem turning point predictor, 409 turning points, 407 two−layer networks, 92 two−thirds rule, 107, 244, 245, 269 U Umano, 486 Unemployment Rate, 387 undertrained network, 329 uniform distribution, 77 union, 32 Unipolar Binary Bi−directional Associative Memory, 212 unit
C++ Neural Networks and Fuzzy Logic:Preface Index
452 circle, 299 hypercube, 218 unit length, 273 universal set, 33 universe of discourse, 498, 499 unsupervised , 107 competitive learning, 271 learning, 5, 110, 115, 117, 121 training, 107, 110
value
fit value, 32 threshold value, 16, 52, 66, 77, 86, 87, 90, 101, 128, 456 variable external, 28 global, 28 vector, 17 codebook vectors, 116 column vector, 97, 104, 181 fit vector, 32, 33 heterassociated, 181 input vector, 53, 71, 272, 112 normalization of, 272 potentially associated, 181 quantization, 302 row vector, 97, 181 weight vector, 9, 96 vector pairs, 181 vertex, 88 vertices, 88 vigilance parameter, 107, 243, 245, 247, 262 virtual, 24, 139 trading, 377 visibility, 26 visible, 53 W walk−forward methodology, 408 Wall Street Journal, 388 Wasserman, 516 weight matrix, 9, 17, 51, 65, 97, 181, 183 weight sharing, 375 weight surface, 113 weight update, 276 weight vector, 9, 96 quantizing, 307 C++ Neural Networks and Fuzzy Logic:Preface Index
453 weighted sum, 2, 3, 19, 271 weights , 4, 181 bottom−up, 250 connection , 89, 98 top−down, 250 Werbos, 103 Wetherell, 514 Widrow, 102, 112 winner indexes, 298 winner, 98 neuron, 323 winner−take−all, 97, 98, 115, 116, 243, 271, 274 World Wide Web, 388 Wu, 515
X XOR function, 83−85, 87 Y Yan, 473, 497 Yu, 212, 214 Yuret, 410 Z zero pattern, 65 zero−one programming problem, 471 Zipser, 374 Table of Contents Copyright © IDG Books Worldwide, Inc. C++ Neural Networks and Fuzzy Logic:Preface Index 454 Download 1.14 Mb. Do'stlaringiz bilan baham: 
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