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.
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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
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DOI: https://doi.org/10.1007/978-3-540-30132-5_104
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