C++ Neural Networks and Fuzzy Logic: Preface

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

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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

M

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

N

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

O

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

Q

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

V

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

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C++ Neural Networks and Fuzzy Logic:Preface

Index

454