86. | C++ Neural Networks and Fuzzy Logic

C++ Neural Networks and Fuzzy Logic
by Valluru B. Rao
M&T Books, IDG Books Worldwide, Inc.
ISBN: 1558515526 Pub Date: 06/01/95

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Generalization versus Memorization

As mentioned in Chapter 11, you actually don’t desire the exact replication of the input pattern for the weight vector. This would amount to memorizing of the input patterns with no capacity for generalization.

For example, a typical use of this alphabet classifier system would be to use it to process noisy data, like handwritten characters. In such a case, you would need a great deal of latitude in scoping a class for a letter A.

Adding Characters

The next step of the program is to add characters and see what categories they end up in. There are many alphabetic characters that look alike, such as H and B for example. You can expect the Kohonen classifier to group these like characters into the same class.

We now modify the input.dat file to add the characters H, B, and I. The new input.dat file is shown as follows.

 0 0 1 0 0   0 1 0 1 0  1 0 0 0 1  1 0 0 0 1  1 1 1 1 1  1 0 0 0 1  1 0 0 0 1 1 0 0 0 1   0 1 0 1 0  0 0 1 0 0  0 0 1 0 0  0 0 1 0 0  0 1 0 1 0  1 0 0 0 1 1 0 0 0 1   1 0 0 0 1  1 0 0 0 1  1 1 1 1 1  1 0 0 0 1  1 0 0 0 1  1 0 0 0 1 1 1 1 1 1   1 0 0 0 1  1 0 0 0 1  1 1 1 1 1  1 0 0 0 1  1 0 0 0 1  1 1 1 1 1 0 0 1 0 0   0 0 1 0 0  0 0 1 0 0  0 0 1 0 0  0 0 1 0 0  0 0 1 0 0  0 0 1 0 0

The output using this input file is shown as follows.

 —————————————————————————-        done ——>average dist per cycle = 0.732607 <—- ——>dist last cycle = 0.00360096 <—- ->dist last cycle per pattern= 0.000720192 <—- ——————>total cycles = 37 <—- ——————>total patterns = 185 <—- —————————————————————————-

The file kohonen.dat with the output values is now shown as follows.

 cycle   pattern    win index   neigh_size    avg_dist_per_pattern ————————————————————————————————————————————————————————————————— 0       0          69          5             100.000000 0       1          93          5             100.000000 0       2          18          5             100.000000 0       3          18          5             100.000000 0       4          78          5             100.000000 1       5          69          5             0.806743 1       6          93          5             0.806743 1       7          18          5             0.806743 1       8          18          5             0.806743 1       9          78          5             0.806743 2       10         69          5             0.669678 2       11         93          5             0.669678 2       12         18          5             0.669678 2       13         18          5             0.669678 2       14         78          5             0.669678 3       15         69          5             0.469631 3       16         93          5             0.469631 3       17         18          5             0.469631 3       18         18          5             0.469631 3       19         78          5             0.469631 4       20         69          5             0.354791 4       21         93          5             0.354791 4       22         18          5             0.354791 4       23         18          5             0.354791 4       24         78          5             0.354791 5       25         69          5             0.282990 5       26         93          5             0.282990 5       27         18          5             0.282990 ... 35      179        78          5             0.001470 36      180        69          5             0.001029 36      181        93          5             0.001029 36      182        13          5             0.001029 36      183        19          5             0.001029 36      184        78          5             0.001029

Again, the network does not find a problem in classifying these vectors.

Until cycle 21, both the H and the B were classified as output neuron 18. The ability to distinguish these vectors is largely due to the small tolerance we have assigned as a termination criterion.

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