| ### Index
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Russell D. Reed and Robert J. Marks II
Copyright © 1999 Massachusetts Institute of Technology

## Index

### M

Marchand's algorithm, 209-212
Master units, 206-209
Mean squared error (MSE) function, 9, 50, 155
dynamic node creation and, 200
error surface and, 117, 123, 129
generalization and, 239, 243, 253, 259, 261, 272, 276
initialization and, 110
learning rate and, 71, 73, 77, 139
pattern weighting and, 151
Widrow-Hoff learning rule and, 29-30
Meiosis networks, 212-213
Meta-optimization, 157
Minima. See Local minima
Mirror symmetry problem, 211
MLP. See Multilayer perception
Model mismatch, 248
Modularity, 255
Momentum, 62-63
algorithm variations and, 137, 140, 142, 144
classical optimization and, 165
frequency response and, 93
gradient correlation and, 150-151
impulse response and, 91-92, 94
inertia and, 89
learning rate and, 71, 72-77, 80, 87-90, 92
small-signal analysis and, 90-95
step response and, 92, 94-95
training time and, 74-77, 85-95
values for, 71
MSE. See Mean squared error function
Multilayer networks, importance of, 19
Multilayer perceptron (MLP)
as cascade of single-layer perceptrons, 31
compared to real nervous systems, 3-4
component layers of, 31
definition of, 29
introduction to, 2-6
representational capabilities of, 31-47
universal approximation by, 35-38
Mutation, 186, 187, 188-189

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