C++ Neural Networks and Fuzzy Logic: Preface

Input Neuron #Hidden Layer Neuron #Connection Weight

111

120.1

13−1

211

22−1

23−1

310.2

320.3

330.6

Now we give, in Table 5.5, the weights for the connections between the three hidden−layer neurons and the

output neuron.

Table 5.5 Weights for Connection Between the Hidden−Layer Neurons and the Output Neuron

Hidden Layer Neuron #Connection Weight

C++ Neural Networks and Fuzzy Logic:Preface

Details

86

---

10.6

30.3

30.6

It is not apparent whether or not these weights will do the job. To determine the activations of the

hidden−layer neurons, you need these weights, and you also need the threshold value at each neuron that does

processing. A hidden−layer neuron will fire, that is, will output a 1, if the weighted sum of the signals it

receives is greater than the threshold value. If the output neuron fires, the function value is taken as +1, and if

it does not fire, the function value is –1. Table 5.6 gives the threshold values. Figure 5.1b shows the neural

network with connection weights and threshold values.

Figure 5.1b

  Neural Network for Cube Example