C++ Neural Networks and Fuzzy Logic: Preface

Further Comments on the Program and Its Output

Let us recall our previous discussion of this example in Chapter 1. What does the network give as output if we

present a pattern different from both A and B? If C = (0, 1, 0, 0) is the input pattern, the activation (dot

products) would be –3, 0, –3, 3 making the outputs (next state) of the neurons 0,1,0,1, so that B would be

recalled. This is quite interesting, because if we intended to input B, and we made a slight error and ended up

presenting C instead, the network would recall B. You can run the program by changing the pattern to 0, 1, 0,

0 and compiling again, to see that the B pattern is recalled.

Another element about the example in Chapter 1 is that the weight matrix W is not the only weight matrix that

would enable the network to recall the patterns A and B correctly. If we replace the 3 and –3 in the matrix

with 2 and –2, respectively, the resulting matrix would facilitate the same performance from the network. One

C++ Neural Networks and Fuzzy Logic:Preface

Further Comments on the Program and Its Output

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way for you to check this is to change the wt1, wt2, wt3, wt4 given in the program accordingly, and compile

and run the program again. The reason why both of the weight matrices work is that they are closely related.

In fact, one is a scalar (constant) multiple of the other, that is, if you multiply each element in the matrix by

the same scalar, namely 2/3, you get the corresponding matrix in cases where 3 and –3 are replaced with 2 and

–2, respectively.

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C++ Neural Networks and Fuzzy Logic:Preface

Further Comments on the Program and Its Output

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