In the previous chapters, we learned the general theory of neural networks, which resemble the human brain in terms of a network of computation units that are interconnected. The neural networks are trained by adjusting the weights on the synapses (connectors). As we have seen, the neural network can be trained to solve classification problems such as image recognition. The neural networks accept crisp input and adjust weights to produce output values (classification into a class). However, as we have seen in this chapter, the real-world input have a degree of fuzziness in the input as well as a degree of vagueness for the output.
The membership of the input and output variables in a specific cluster or a type is represented with ...
