By Christopher MacLeod
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Additional resources for An Introduction to Practical Neural Networks and Genetic Algorithms For Engineers and Scientists
Output after network has relaxed Input to network This means that the network has been able to store the correct (uncorrupted) pattern – in other words it has a memory. Because of this these networks are sometimes called Associative Memories or Hopfield Memories. 2 Training – one shot method It is possible to create many types of network which use feedback as part of their structure. Such networks may be trained using variations of Back Propagation2 or Genetic Algorithms as described in later chapters.
4. 4, that the book ‘Perceptrons’ by Minsky and Papert pointed out some limitations with basic neurons, which caused researchers in the field to lose interest in ANNs in the 1970s. One of the main arguments used in the book is that a simple neuron cannot simulate a two input Exclusive OR (XOR) gate. This is known as the Exclusive OR problem. 1. 1, two input neuron. 2. 2, the function of the neuron. Line representing neuron function (equation of straight line given above) Y 0,1 1,1 The neuron will produce a “1” for every input combination above the line.
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.