C++ Neural Networks and Fuzzy Logic: Preface

Input/Output for percept.cpp

There are two data files used in this program. One is for setting up the weights, and the other for setting up the

input vectors. On the command line, you enter the program name followed by the weight file name and the

input file name. For this discussion (also on the accompanying disk for this book) create a file called

weight.dat, which contains the following data:

  2.0 3.0 3.0 2.0

  3.0 0.0 6.0 2.0

These are two weight vectors. Create also an input file called input.dat with the two data vectors below:

  1.95 0.27 0.69 1.25

  0.30 1.05 0.75 0.19

During the execution of the program, you are first prompted for the number of vectors that are used (in this

case, 2), then for a threshold value for the input/weight vectors (use 7.0 in both cases). You will then see the

following output. Note that the user input is in italic.

  percept weight.dat input.dat

THIS PROGRAM IS FOR A PERCEPTRON NETWORK WITH AN INPUT LAYER OF 4

NEURONS, EACH CONNECTED TO THE OUTPUT NEURON.

THIS EXAMPLE TAKES REAL NUMBERS AS INPUT SIGNALS

please enter the number of weights/vectors

2

this is vector # 1

please enter a threshold value, eg 7.0

7.0

weight for neuron 1 is  2           activation is 3.9

weight for neuron 2 is  3           activation is 0.81

C++ Neural Networks and Fuzzy Logic:Preface

Comments on Your C++ Program

71

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weight for neuron 3 is  3           activation is 2.07

weight for neuron 4 is  2           activation is 2.5

activation is  9.28

the output neuron activation exceeds the threshold value of 7

 output value is 1

this is vector # 2

please enter a threshold value, eg 7.0

7.0

weight for neuron 1 is  3           activation is 0.9

weight for neuron 2 is  0           activation is 0

weight for neuron 3 is  6           activation is 4.5

weight for neuron 4 is  2           activation is 0.38

activation is  5.78

the output neuron activation is smaller than the threshold value of 7

output value is 0

Finally, try adding a data vector of (1.4, 0.6, 0.35, 0.99) to the data file. Add a weight vector of ( 2, 6, 8, 3) to

the weight file and use a threshold value of 8.25 to see the result. You can use other values to experiment also.