A Neural Network and an Expert System for the Analysis of Basic Metric Characteristics of Composite Measuring Instruments
Keywords:
neural networks, expert systems, theory of measurementAbstract
A modification of Hopfield neural network is implemented, and designed to estimate partial image of items and then to compute the following metric characteristics of composite measuring instruments (e.g. motor, psychological, etc.): (1) psi4, Momirović's modification of Kaiser-Rice measure of representativeness; (2) lambda6, Guttman sixth lower bound to reliability of summation score; (3) beta6, Momirović lower bound to reliability of component score; (4) h2, Momirović estimation of homogeneity of items set. Formal definitions and derivations of the implemented measures can be found in any modern textbook of measurement theory. Because of a modest educational level of most professionals in the theory of measurement, a primitive expert system is added to the main body of program in order to facilitate the interpretation of the obtained results in spite of a negative opinion of the first author about the real utility of interpretative expert systems.
References
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79, 2554–2558.
Momirović, K. (1989a). Interpretative expert systems in statistics. In Statistical data analysis (pp. 10–19). Sofia.
Momirović, K. (1989b). On the real value of diagnostic expert systems. Proceedings of the 13th Informatics Technologies Conference, 207, 1–6.
Momirović, K., Wolf, B., & Popović, D. (1999). Uvod u teoriju merenja: Vol. 1. Metrijske karakteristike kompozitnih mernih instrumenata. Univerzitet u Prištini.
Wolf, B., Džamonja, Z., Radovanović, D., & Momirović, K. (1992). Test oralne agresivnosti SIGMA-3. Zbornik Instituta za kriminološka i sociološka istraživanja, 21(1–2), 210–213.
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