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Artificial Neural Network Approach to Temperature Control

Artificial Neural Network Approach to Temperature Control. Presented by Jeff Boettcher. Project Outline. Describe the project purpose Discuss method used to complete the project Present results of experiments Conclusion / Discussion. Project Purpose.

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Artificial Neural Network Approach to Temperature Control

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  1. Artificial Neural Network Approach to Temperature Control Presented by Jeff Boettcher

  2. Project Outline • Describe the project purpose • Discuss method used to complete the project • Present results of experiments • Conclusion / Discussion

  3. Project Purpose A reservoir of water is desired to be held at a constant temperature. There are many common approaches to control problems, but here an artificial neural network approach is explored.

  4. Solution Method (General) A temperature sensor was selected, and its output conditioned so that the voltage produced could be easily read into the computer through a data acquisition card and LabVIEW. The water temperature was monitored and, if less than the desired temperature of 75°C, the heater was turned on. If greater than 75°C, the heater was turned off.

  5. Solution Method (SVM) It was known that temperatures under 75°C desired an ON output to the heater, and over 75°C desired an OFF to the heater. With this two state output, a set of SVM training data was easily determined. This data can be seen in the diagram slides. Once “alpha” and “bias” terms were found by SVC training, they were fed into the classification along with the current temperature at each second to produce the binary decision of whether to turn the heater ON or OFF. All SVM calculations performed through the LabVIEW “Run Matlab script” function, with calls to Steve Gunn’s SVM toolbox.

  6. LabVIEW Front Panel

  7. LabVIEW Diagram (Main)

  8. LabVIEW Diagram (Training)

  9. LabVIEW Diagram (Classify)

  10. Results It was found that SVM classification works well for this type of control. The temperature vs. time plot of the control scheme is shown to the right. (Jaggedness of line comes from integer temperature values.)

  11. Conclusion / Discussion While SVM decision making is not claimed to be the best control method available, it is clear by the results that this is a feasible and very functional control method.

  12. References Steve Gunn’s support vector machine toolbox http://www.isis.ecs.soton.ac.uk/resources/svminfo/ Yu Hen Hu’s support vector machine course notes http://www.cae.wisc.edu/~ece539/spring00/notes/Classnotes/svm.pdf

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