HVAC Control in the New Millennium.pdf - HVAC.Amickracing

HVAC Control in the New Millenniumpass filter on the weight differences that allows faster learning on lowlearning coefficients. Other features to expedite convergence includealgorithms for random weight generation, introduction of noise as thenetwork learns, and algorithms to jog the weights randomly.Hidden layers act as layers of abstraction and help the networkgeneralize and memorize. Most control applications require only onehidden layer.One way of determining whether multiple layers or multiple slabsimprove the performance of a network is experimentation. Determiningthe number of nodes in a hidden layer is also an experimental exercise.More complex relationships require more processing elements inthe hidden layer. Too few nodes in the hidden layer prevents the networkfrom properly mapping inputs to outputs. Too many nodes promotesmemorization and inhibits generalization.Memorization occurs when the patterns presented to the networkare reproduced exactly without extracting any salient features. The networkis then unable to process new patterns correctly because it has notdiscovered the proper relationships.Preparing the data includes transforming inputs into the properform such as ratios or classes and data types. How the data is representedand translated plays a role in the network’s ability to understandduring training. Data may be continuous, digital, time-oriented or static.Data can be naturally grouped, may be represented as actual amounts orchanges in amounts, or may be evenly or unevenly distributed over theentire range. When naturally occurring groups appear, binary categoriesare often the best method for making correlations.All data needs to be normalized in order for the transform functionto operate. Data may be scaled between minimum and maximum rangesor be set between a predefined range.Often the network is trained on one set of data (training data) andverified with a different set of data (recall data). Once a network istrained on a set of data, it is used to predict results based upon new setsof gathered data.Statistical tools can help determine how the network produces thecorrect outputs. Small weights indicate that the processing elements arenot influencing the outcome of the network. These nodes can then beeliminated and the network retrained. Large weights can indicate toomuch dependence upon a particular input, indicating some degree ofmemorization.©2001 by The Fairmont Press, Inc. All rights reserved.