The role of fuzzy logic and soft computing in the conception and design of intelligent systems

Abstract

The past three years have witnessed a significant increase in the rate of growth of MIQ (Machine Intelligence Quotient) of consumer products and industrial systems.

There are many factors which account for the increase in question but the most prominent among them is the rapidly growing use of soft computing and especially fuzzy logic in the conception and design of intelligent systems.

The principal aim of soft computing is to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness and low solution cost. At this juncture, the principal constituents of soft computing (SC) are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming genetic algorithms, belief networks, chaotic systems, and parts of learning theory. In the triumvirate of SC, FL is concerned in the main with imprecision, NN with learning and PR with uncertainty. In large measure, FL, NN and PR are complementary rather than competitive. It is becoming increasingly clear that in many cases it is advantageous to employ FL, NN and PR in combination rather than exclusively. A case in point is the growing number of neurofuzzy consumer products and systems which employ a combination of fuzzy logic and neural network techniques.

As one of the principal constituents of soft computing, fuzzy logic is playing a key role in the conception and design of what might be called high MIQ (Machine Intelligence Quotient) systems. There are two concepts within FL which play a central role in its applications. The first is that of a linguistic variable, that is, a variable whose values are words or sentences in a natural or synthetic language. The other is that of a fuzzy if-then rule in which the antecedent and consequent are propositions containing linguistic variables. The essential function served by linguistic variables is that of granulation of variables and their dependencies. In effect, the use of linguistic variables and fuzzy if-then rules results — through granulation — in soft data compression which exploits the tolerance for imprecision and uncertainty. In this respect, fuzzy logic mimics the crucial ability of the human mind to summarize data and focus on decision-relevant information.