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

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<strong>HVAC</strong> <strong>Control</strong> <strong>in</strong> <strong>the</strong> <strong>New</strong> <strong>Millennium</strong>Future TrendsExpert systems open a new dimension <strong>in</strong> <strong>the</strong> way computers canbe used for <strong>HVAC</strong>. Expert systems technology is be<strong>in</strong>g <strong>in</strong>tegrated <strong>in</strong>to<strong>the</strong> architecture of many distributed control systems and networkedprogrammable controller systems. Self-tun<strong>in</strong>g PID controllers use expertsystem technology. A boiler might <strong>in</strong>corporate a troubleshoot<strong>in</strong>g anddiagnostic expert system as part of <strong>the</strong> control system for <strong>the</strong> boiler.Expert system applications <strong>in</strong>clude sensor test<strong>in</strong>g and validation,control system design validation, performance evaluation, diagnostics,on-l<strong>in</strong>e tun<strong>in</strong>g and statistical control and analysis.Neural NetworksNeural networks such as expert systems, fuzzy logic, robotics,natural language process<strong>in</strong>g, and mach<strong>in</strong>e vision, are parts of <strong>the</strong> overalltechnology umbrella known as artificial <strong>in</strong>telligence (AI). Neural networksattempt to mimic <strong>the</strong> structures and process of biological neuralsystems. They provide a powerful analysis technique for complex process<strong>in</strong>gof large amounts of <strong>in</strong>put/output <strong>in</strong>formation. They have <strong>the</strong>ability to generalize or form concepts.Artificial neural networks (ANNs) are a useful <strong>in</strong>formation abstraction<strong>in</strong> model<strong>in</strong>g <strong>in</strong>telligence <strong>in</strong> control systems. Neural networksmay be thought of as a functional mapp<strong>in</strong>g of <strong>in</strong>puts to outputs us<strong>in</strong>gan <strong>in</strong>terconnected network of nodes. Weights are given to <strong>the</strong> node <strong>in</strong>terconnectionsto achieve <strong>the</strong> desired mapp<strong>in</strong>g.Artificial neural networks are a cognitive <strong>in</strong>formation process<strong>in</strong>gstructure based upon <strong>the</strong> models of bra<strong>in</strong> function. It is a highly paralleldynamic system that processes <strong>in</strong>formation us<strong>in</strong>g <strong>the</strong> state response to<strong>in</strong>puts.The advantage of neural networks is <strong>in</strong> <strong>the</strong>ir ability to learn arbitraryfunction mapp<strong>in</strong>g with little or no prior knowledge about <strong>the</strong> functionitself. They provide <strong>the</strong> capability to do black-box model<strong>in</strong>g of aprocess given only <strong>the</strong> Input/Output data.These networks have proved to be robust, resilient and capable ofadaptive learn<strong>in</strong>g. The disadvantage is that <strong>the</strong> knowledge is implied by<strong>the</strong> network connection weights.Neural networks have been used as virtual or soft sensors to <strong>in</strong>fer©2001 by The Fairmont Press, Inc. All rights reserved.

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