C++ Neural Networks and Fuzzy Logic: Preface

Figure 1.8

  A partly lost Pattern of Figure 1.7.

Summary

In this chapter we introduced a neural network as a collection of processing elements distributed over a finite

number of layers and interconnected with positive or negative weights, depending on whether cooperation or

competition (or inhibition) is intended. The activation of a neuron is basically a weighted sum of its inputs. A

threshold function determines the output of the network. There may be layers of neurons in between the input

layer and the output layer, and some such middle layers are referred to as hidden layers, others by names such

as Grossberg or Kohonen layers, named after the researchers Stephen Grossberg and Teuvo Kohonen, who

proposed them and their function. Modification of the weights is the process of training the network, and a

network subject to this process is said to be learning during that phase of the operation of the network. In

some network operations, a feedback operation is used in which the current output is treated as modified input

to the same network.

You have seen a couple of examples of a Hopfield network, one of them for pattern recognition.

Neural networks can be used for problems that can’t be solved with a known formula and for problems with

incomplete or noisy data. Neural networks seem to have the capacity to recognize patterns in the data

presented to it, and are thus useful in many types of pattern recognition problems.

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C++ Neural Networks and Fuzzy Logic:Preface

Summary

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