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

Artificial Intelligence, Fuzzy Logic and ControlNeural Network OperationA neural network processing element has many input paths whichare individually multiplied by a weight and then summed. A nonlineartransfer function known as a squashing function is applied to the resultto calculate each processing element’s output.The transfer function must provide both nonlinearity and stabilityto the network. Typical transfer functions include the sigmoid transferfunction which is most often used in back-propagation networks andTanH which is a bipolar version of the sigmoid.The output value of the transfer function is usually passed directlyto the output path of the processing element. The output path is thenconnected to input paths of other processing elements through connectionweights. The weights and connections form the memory or knowledgeof the neural net. Since each connection has a correspondingweight, the signals on the input lines to a processing element are modifiedby these weights prior to being summed. Thus, the summationfunction is a weighted summation.A neural network consists of many processing elements joinedtogether. A typical network consists of a sequence of layers with connectionsbetween successive layers. A minimum of two layers is required.These are the input buffer where data is presented and the output layerwhere the results are held. Many networks also use intermediate layerscalled hidden layers.Applications for neural networks need an abundance of historicaldata or examples with data dependence on several interacting parameters.Back-propagation neural networks have been used in chemicalprocess control to predict boiling points.The type and number of processing elements, the number of slabs,the number of layers, the connectivity of the layers, the transfer function,the learning algorithm or rule are all parameters of the network.Other parameters include the learning threshold and learning coefficientsand learning schedule.The learning schedule is a breakpoint table that allows the learningcoefficient to decay after a number of learning passes. The higher thelearning coefficient, the faster the learning. However, the higher thelearning coefficient, the slower the convergence. Therefore, if the learningcoefficient can be reduced as training proceeds, high learning andfast convergence can take place. The momentum factor acts as a low-©2001 by The Fairmont Press, Inc. All rights reserved.