By Simon Kendal, Malcolm Creen
The authors use a fresh and novel 'workbook' writing variety which provides the e-book a really functional and simple to take advantage of believe. It contains methodologies for the advance of hybrid details platforms, covers neural networks, case dependent reasoning and genetic algorithms in addition to professional platforms. various tips to net established assets and present examine also are integrated. The content material of the e-book has been effectively utilized by undergraduates worldwide. it's aimed toward undergraduates and a powerful maths history isn't really required.
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Extra resources for An Introduction to Knowledge Engineering
Example text
Write down what you consider to be the main stages in the demonstration of the Nenet software when applied to the example problem illustrated in the demo. Under what circumstances could one of the stages has been skipped? Why is this possible? How is ‘Training Length’ measured? Feedback 12 You should have been able to identify the following stages: r Initialise a new map r Set initialisation parameters r Train the map to order the reference vectors of the map neurons (not if using linear initialisation) r Train the map (again) r Test the map r Set test parameters.
R Retain details of the current (now hopefully solved) problem as a new case for future reference. Cases do not need to be understood by the knowledge engineer in order to be stored.
Show whether or not the neuron works for Richard, then Jennifer. Feedback 8 The relevant weights to use for Richard are: Criteria Hours driving experience > 40 Age > 22 Power steering in car? Qualified instructor? 5. 7); they will be increased by 10%. Note that the 10% increase is still not sufficient to predict Richard passing. It would take several instances in which the weights need to be increased in order to end up with a better performing system. The relevant weights to use for Jennifer are now: Criteria Hours driving experience > 40 Age > 22 Power steering in car?