Download Perceptual Computing by Jerry M. Mendel, Dongrui Wu(auth.), David B. Fogel(eds.) PDF

By Jerry M. Mendel, Dongrui Wu(auth.), David B. Fogel(eds.)

Explains for the 1st time how "computing with phrases" can reduction in making subjective judgments

Lotfi Zadeh, the daddy of fuzzy good judgment, coined the word "computing with phrases" (CWW) to explain a strategy during which the items of computation are phrases and propositions drawn from a typical language. Perceptual Computing explains the right way to enforce CWW to assist within the very important quarter of constructing subjective judgments, utilizing a technique that results in an interactive device—a "Perceptual Computer"—that propagates random and linguistic uncertainties into the subjective judgment in a manner that may be modeled and saw via the judgment maker.

This booklet makes a speciality of the 3 parts of a Perceptual Computer—encoder, CWW engines, and decoder—and then offers unique purposes for every. It makes use of period type-2 fuzzy units (IT2 FSs) and fuzzy good judgment because the mathematical automobile for perceptual computing, simply because such fuzzy units can version first-order linguistic uncertainties while the standard form of fuzzy units can't. Drawing upon the paintings on subjective judgments that Jerry Mendel and his scholars accomplished over the last decade, Perceptual Computing exhibits readers the way to:

  • Map word-data with its inherent uncertainties into an IT2 FS that captures those uncertainties

  • Use uncertainty measures to quantify linguistic uncertainties

  • examine IT2 FSs by utilizing similarity and rank

  • Compute the subsethood of 1 IT2 FS in one other such set

  • mixture disparate info, starting from numbers to uniformly weighted durations to nonuniformly weighted durations to phrases

  • mixture multiple-fired IF-THEN ideas in order that the integrity of observe IT2 FS types is preserved

loose MATLAB-based software program is additionally on hand on-line so readers can observe the technique of perceptual computing instantly, or even attempt to increase upon it. Perceptual Computing is a crucial go-to for researchers and scholars within the fields of man-made intelligence and fuzzy common sense, in addition to for operations researchers, choice makers, psychologists, computing device scientists, and computational intelligence experts.Content:
Chapter 1 advent (pages 1–34):
Chapter 2 period Type?2 Fuzzy units (pages 35–63):
Chapter three Encoding: From a note to a Model—The Codebook (pages 65–116):
Chapter four interpreting: From FOUs to a advice (pages 117–144):
Chapter five Novel Weighted Averages as a CWW Engine (pages 145–173):
Chapter 6 IF–THEN ideas as a CWW Engine—Perceptual Reasoning (pages 175–197):
Chapter 7 aiding in Making funding Choices—Investment Judgment consultant (IJA) (pages 199–233):
Chapter eight helping in Making Social Judgments—Social Judgment consultant (SJA) (pages 235–257):
Chapter nine helping in Hierarchical choice Making—Procurement Judgment consultant (PJA) (pages 259–282):
Chapter 10 aiding in Hierarchical and allotted selection Making—Journal e-book Judgment consultant (JPJA) (pages 283–310):
Chapter eleven Conclusions (pages 311–313):

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Extra info for Perceptual Computing

Sample text

This is called an expressive way to summarize the LWA rather than a computational way to summarize the LWA, because the LWA is not computed by multiplying, adding, and dividing IT2 FSs. It is more complicated than that. How to actually compute Y~LWA is described in Chapter 5, and, somewhat surprisingly, KM algorithms are the bread-and-butter tools for the computations. The exact details of the computations are not needed here. What is needed is the recognition that given ~ , it is possible to compute FOU(Y~ ).

A) Type-1 membership function, (b) blurred type-1 membership function, and (c) FOU for an IT2 FS (Mendel, 2001; © 2001, Prentice-Hall). ~ Although these functions are shown as triangles in Fig. 13, they can UMF(A). have many other shapes, for example, trapezoids or Gaussian. It is very easy to compute the union, intersection, and complement of IT2 FSs just in terms of simple T1 FS operations that are performed only on LMFs or UMFs of IT2 FSs. This makes such FSs very useful for practical applications.

3) For CWW this is a very plausible assumption because, as explained in Chapter 3, only univariate word MFs are established from data. 2. The support of a T1 FS A is the crisp set of all points x ʦ DA such that ␮A(x) > 0. A T1 FS whose support is a single point in DA with ␮A(x) = 1 is called a (type-1) fuzzy singleton. 3. A normal T1 FS is one for which supxʦDA␮A(x) = 1. 4. A T1 FS A is convex if and only if ␮A(␭x1 + (1 – ␭)x2) Ն min[␮A(x1), ␮A(x2)], where x1, x2 ʦ DA and ␭ ʦ [0, 1] [Klir and Yuan (1995)].

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