An Introduction to Practical Neural Networks and Genetic by Christopher MacLeod

By Christopher MacLeod

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Obviously then, this network requires a little more thought to set up compared to some of the others. The weights and inputs need to be processed so that they are all vectors of length one. The network will also work better if the weights are equally distributed around the unit circle. Care also needs to be taken over the choice of learning rate η because one doesn’t want the weight vector to move onto the input vector in one go, we’d rather it find an average position in the middle of the various (slightly different) inputs corresponding to that particular pattern.

1. A good example of this type of file format is RAW, where each pixel is represented by a number, the range of which depends on the grey scale resolution of the image. As an example, in an 8 bit image, each pixel is represented by a number between 0 and 255 (decimal), this being a measure of the brightness of that pixel, with 255 being white and 0 being black. So a 40 by 40 pixel image is represented by 1600 eight bit numbers. 1, a simple image and its representation in RAW format. Image as 8 bit RAW data: Image as 1 bit RAW data: 0 255 255 0 1 1 255 0 255 255 255 0 1 0 1 1 1 0 Typically, if the image is in a complex format, then it must be decompressed, headers and footers deleted and colour information discarded if not required.

Competitive networks In this chapter we’ll look at a different type of network called the Competitive Network. This and its relations are sometimes also called Kohonen, Winner Takes All or Self Organising networks. They are used to identify patterns in data, even when the programmer may not know the nature of the pattern. Their operation is best illustrated by example. 1. 1, a network of three neurons. Input 1 Input 2 1 2 3 Output 1 Output 2 Output 3 We’ll not worry too much about the set up of the weights at the moment except to say that they are all essentially random.

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