Application of Neural Networks and Fuzzy Logic to the Calibration of the Seven-Hole Probe
Author:
Rediniotis O. K.1, Chrysanthakopoulos G.2
Affiliation:
1. Aerospace Engineering Department, Texas A&M University, College Station, TX 77843 2. Electrical Engineering Department, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
Abstract
The theory and techniques of Artificial Neural Networks (ANN) and Fuzzy Logic Systems (FLS) are applied toward the formulation of accurate and wide-range calibration methods for such flow-diagnostics instruments as multi-hole probes. Besides introducing new calibration techniques, part of the work’s objective is to: (a) apply fuzzy-logic methods to identify systems whose behavior is described in a “crisp” rather than a “linguistic” framework and (b) compare the two approaches, i.e., neural network versus fuzzy logic approach, and their potential as universal approximators. For the ANN approach, several network configurations were tried. A Multi-Layer Perceptron with a 2-node input layer, a 4-node output layer and a 7-node hidden/middle layer, performed the best. For the FLS approach, a system with center average defuzzifier, product-inference rule, singleton fuzzifier, and Gaussian membership functions was employed. The Fuzzy Logic System seemed to outperform the Neural Network/Multi-Layer Perceptron.
Publisher
ASME International
Subject
Mechanical Engineering
Reference14 articles.
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