Approximation space for intelligent system design patterns

Abstract

This article introduces an approximation space for graded acceptance of proposed models for intelligent system design relative to design patterns that 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 ground for concluding to what degree a particular model for an intelligent system design is a part of a set of a set of models representing a standard. The basic assumption made in this research is that every system design can be approximated relative to a standard, and it is possible to prescribe conditions for the construction of a set of acceptable design models. It is also possible to measure the degree that a proposed set of design models is a member of a set of design models that conform to a standard. The neuron and sensor behavioral design patterns are briefly considered by way of illustration of design model approximation. A satisfaction-based approximation space for patterns extracted from intelligent system design models is introduced.