By Christopher MacLeod
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Extra info for An Introduction to Practical Neural Networks and Genetic Algorithms For Engineers and Scientists
As an example, consider the automatic control of a washing machine. Inputs to the network could be the weight of load, the amount of washing power, the hardness of the water and the program chosen by the user. Outputs could be water temperature, washing cycle type and length of each stage. The beauty of this type of system is that the network will interpolate its responses to make allowances for load types that it hasn’t been trained with (this is just another type of “noisy input”). Likewise, one can easily think up many similar applications with vacuum cleaners, dish-washers and ovens or other cooking appliances.
This format might include nonlinear coding and/or be compressed. Popular examples of such formats are JPEG and GIF in the case of still images and in the case of moving images, MOV, AVI or MPEG formats. All these require pre-processing before they are suitable for use in a network. 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.
2. 3. 4. 5. 4 Plot the weight vectors on a graph paper. Make the vectors one unit in length and replot. 8) one unit in length and plot on graph. Run input through the network and confirm winning neuron. 6) and confirm that weight vector has moved towards input vector. 1 to recognise two patterns: Pattern 1 Pattern 2 Use different initial weight vectors if necessary. 3 Advanced competitive networks The competitive networks shown above are not usually used in such a simple form, but are seen as part of a larger and more complex network.