Abstract
This article introduces an approach to measuring the degree to which, intelligent system models conform to a design standard. A fundamental problem in system design is that feature values extracted from experimental design models tend not to match exactly patterns associated with standard design models. It is not generally known how to measure the extent that a particular intelligent system design conforms to a standard design pattern. The rough set approach introduced by Zdzisław Pawlak provides a basis for concluding and more specifically measuring to what degree a particular model for an intelligent system design is a part of a set of models representing a standard. Measurements are carried out within satisfaction-based approximation spaces defined in the context of rough sets. The neuron and sensor behavioral models are briefly considered by way of illustration of the approach to measuring acceptance of an intelligent system design model.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alexander, C., Ishikawa, S., Silverstein, M., Jacobson, M., Fiksdahl-King, S., Angel, I.: A Pattern Language. Oxford University Press, UK (1977)
OMG Unified Modeling Language Specification. Object Management Group, http://www.omg.org
Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing. Techniques for Computing with Words. Springer, Heidelberg (2004)
Pawlak, Z.: Rough sets. International J. Comp. Inform. Science 11, 341–356 (1982)
Peters, J.F.: Design patterns in intelligent systems. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 262–269. Springer, Heidelberg (2003)
Peters, J.F., Ramanna, S.: Intelligent systems design and architectural patterns. In: Proceedings IEEE Pacific Rim Conference on Communication, Computers and Signal Processing (PACRIM 2003), pp. 808–811 (2003)
Peters, J.F., Ramanna, S., Suraj, Z., Borkowski, M.: Rough neurons: Petri net models and applications. In: [3], pp. 471–490
Peters, J.F., Skowron, A., Stepaniuk, J., Ramanna, S.: Towards an ontology of approximate reason. Fundamenta Informaticae 51(2), 157–173 (2002)
Polkowski, L.: Rough Sets. In: Mathematical Foundations, Physica-Verlag, Heidelberg (2002)
Skowron, A.: Toward intelligent systems: Calculi of information granules. In: Hirano, S., Inuiguchi, M., Tsumoto, S. (eds.) Bulletin of the International Rough Set Society, vol. 2(1/2), pp. 9–30 (2001)
Skowron, A., Stepaniuk, J., Peters, J.F.: Rough sets and infomorphisms: Towards approximation of relations in distributed environments. Fundamenta Informaticae 54(2,3), 263–277 (2003)
Skowron, A., Swiniarski, R.W.: Information granulation and pattern recognition. In: [3], pp. 599-636
Stepaniuk, J.: Approximation spaces, reducts and representatives. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 2, pp. 295–306. Physica-Verlag, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peters, J.F., Ramanna, S. (2004). Measuring Acceptance of Intelligent System Models. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_104
Download citation
DOI: https://doi.org/10.1007/978-3-540-30132-5_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
eBook Packages: Springer Book Archive