Learning Fuzzy Logics

Topics: Fuzzy logic, Fuzzy set, Set Pages: 9 (3110 words) Published: August 28, 2013
Fuzzy Logic Introduction
M. Hellmann
a

Laboratoire Antennes Radar Telecom, F.R.E CNRS 2272, Equipe Radar Polarimetrie, Universit´ de Rennes 1, UFR S.P.M, Campus de Beaulieu - Bat. 22, e 263 Avenue General Leclerc, CS 74205, 35042 Rennes Cedex, France

1.I NTRODUCTION Fuzzy Logic was initiated in 1965 [1], [2], [3], by Lotfi A. Zadeh , professor for computer science at the University of California in Berkeley. Basically, Fuzzy Logic (FL) is a multivalued logic, that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc. Notions like rather tall or very fast can be formulated mathematically and processed by computers, in order to apply a more human-like way of thinking in the programming of computers [4]. Fuzzy systems is an alternative to traditional notions of set membership and logic that has its origins in ancient Greek philosophy. The precision of mathematics owes its success in large part to the efforts of Aristotle and the philosophers who preceded him. In their efforts to devise a concise theory of logic, and later mathematics, the so-called ”Laws of Thought” were posited [5]. One of these, the ”Law of the Excluded Middle,” states that every proposition must either be True or False. Even when Parminedes proposed the first version of this law (around 400 B.C.) there were strong and immediate objections: for example, Heraclitus proposed that things could be simultaneously True and not True. It was Plato who laid the foundation for what would become fuzzy logic, indicating that there was a third region (beyond True and False) where these opposites ”tumbled about.” Other, more modern philosophers echoed his sentiments, notably Hegel, Marx, and Engels. But it was Lukasiewicz who first proposed a systematic alternative to the bi–valued logic of Aristotle [6]. Even in the present time some Greeks are still outstanding examples for fussiness and fuzziness, (note: the connection to logic got lost somewhere during the last 2 mileniums [7]). Fuzzy Logic has emerged as a a profitable tool for the controlling and steering of of systems and complex industrial processes, as well as for household and entertainment electronics, as well as for other expert systems and applications like the classification of SAR data. 2. F UZZY S ETS AND C RISP S ETS The very basic notion of fuzzy systems is a fuzzy (sub)set. In classical mathematics we are familiar with what we call crisp sets. For example, the possible interferometric coherence g values are the set X of all real numbers between 0 and 1. From this set X a subset A can be defined, (e.g. all values 0 ≤ g ≤ 0.2). The characteristic function of A, (i.e. this function assigns a number 1 or 0 to each element in X, depending on whether the element is in the subset A or not) is shown in Fig.1. The elements which have been assigned the number 1 can be interpreted as the elements that are in the set A and the elements which have assigned the number 0 as the elements that are not in the set

Figure 1: Characteristic Function of a Crisp Set

A. This concept is sufficient for many areas of applications, but it can easily be seen, that it lacks in flexibility for some applications like classification of remotely sensed data analysis. For example it is well known that water shows low interferometric coherence g in SAR images. Since g starts at 0, the lower range of this set ought to be clear. The upper range, on the other hand, is rather hard to define. As a first attempt, we set the upper range to 0.2. Therefore we get B as a crisp interval B=[0,0.2]. But this means that a g value of 0.20 is low but a g value of 0.21 not. Obviously, this is a structural problem, for if we moved the upper boundary of the range from g =0.20 to an arbitrary point we can pose the same question. A more natural way to construct the set B would be to relax the strict separation between low and not low. This can be done by allowing not only the (crisp) decision...
Continue Reading

Please join StudyMode to read the full document

You May Also Find These Documents Helpful

  • Fuzzy Logic Essay
  • Fuzzy Logic Essay
  • Essay about Fuzzy Logic
  • Essay about Seminar Report on Fuzzy Logic
  • Essay on Fuzzy Control Systems
  • DESIGN OF SELF-TUNING FUZZY CONTROLLER FOR MICRO HYDROPOWER PLANSTS ON IRRIGATION DAMS Essay
  • fuzzy traffic light controller Essay
  • Fuzzy Logic Essay

Become a StudyMode Member

Sign Up - It's Free