30.

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Page 124

Figure 3.11 The weight adjustment rule for counter-propagation. Only weights connecting to the winning neurone (shaded) are adjusted.

maximum weighted sum Sv. The weights connecting the input layer neurones to the winning neurone v are then updated in terms of following rule:

(3.21)

where α, 0 α≤1, is the learning rate, and denotes the weight state at time n. The other weight sets, wz, for , retain their original values. The weight set wv is then again subjected to the following normalisation process to maintain its normalisation state:

(3.22)

For the weight set , connecting the winning neurone v in the hidden layer to the output layer, hv is updated in terms of:

(3.23)

where β is another learning constant, oj is the desired output for output neurone j, and aj is the network output derived from:

(3.24)

[Cover] [Contents] [Index]


Classification Methods for Remotely Sensed Data
Classification Methods for Remotely Sensed Data, Second Edition
ISBN: 1420090720
EAN: 2147483647
Year: 2001
Pages: 354

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