Download Computing with Words in Information/Intelligent Systems 2: by Sanya Mitaim, Bart Kosko (auth.), Prof. Lotfi A. Zadeh, PDF

By Sanya Mitaim, Bart Kosko (auth.), Prof. Lotfi A. Zadeh, Prof. Janusz Kacprzyk (eds.)

The quantity is the 1st one on the planet literature that's a entire, modern account on computing with phrases, a brand new course in extensively perceived clever structures, proposed and recommended through Professor Zadeh, the founding father of fuzzy units concept and fuzzy common sense. traditionally, computing used to be interested in manipulation of numbers. although, lately it turned an increasing number of visible that computing have been encompassing not just this, but additionally manipulation on sign, sounds, photos and textual content; the latter features of computing have gotten an increasing number of proper. besides the fact that, the normal manipulation of textual content (e.g., desktop translation, spell checking, etc.) doesn't hide the mainstream of computing with phrases, usually the illustration and manipulation of propositions from traditional language describing operations and kinfolk. Such difficulties can't be solved by way of traditional tools of good judgment, likelihood concept and numerical research. Fuzzy good judgment is proven to be a good software to address such difficulties.
Computing with phrases may possibly shape a foundation of a computational thought of perceptions encouraged through a amazing human skill to accomplish a large choice of initiatives simply at the foundation of obscure and obscure info expressed in ordinary language.
In Part 2 purposes in a wide range of fields are provided which use the paradigm of computing with phrases, exemplified through reasoning, facts anaylsis, info mining, desktop studying, threat anaylses, reliability and qc, choice making, optimization and regulate, databases, clinical prognosis, enterprise analyses, site visitors administration, energy process making plans, army functions, etc.

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Extra resources for Computing with Words in Information/Intelligent Systems 2: Applications

Example text

If some rules or premises do not fit the structure in the data, the learning algorithm will delete these elements (see section 4). A typical problem in the field of data analysis is the overfitting effect because the data is very noisy and often not available to the necessary extent [11]. The integration of expert knowledge into a neural network can be useful to reduce the effect of overfitting and prevent bad generalization (see section 7). Since rules given by an human expert are generally low dimensional and each rule takes only a small number of input variables into account, the resulting architecture of our neuro-fuzzy-system is very sparse.

Note, that Sdee < 0 and Sine> O. The parameter min asta whose controls the minimal width of the Gaussian membership function is not yet defined. We have to define a value for this constraint to guarantee, that the membership function meets the semantic (see figure (11)) even ifthe width approaches its smallest possible value. This value has to be captured by the following two situations. • , ... S> . • ... • .. 7 1 . 5 sl ble increasing Jo. 1 0 -a input .. Increasing decreasing . 1 0 -a input (b) (a) Fig.

Petkovic, P. Yanker, and C. Faloutsos, "The QBIC Project: Querying Images by Content Using Color, Texture, and Shape," Research Report RJ 9203 (81511)' IBM, February 1993. 22 [42] G. Owen, Game Theory, Academic Press, third edition, 1995. [43] N. R. Pal and J. C. Bezdek, "On Cluster Validity for the Fuzzy c-Means Model," IEEE Transactions on Fuzzy Systems, vol. 3, no. 3, pp. 370-379, August 1995. [44] N. R. Pal, J. C. Bezdek, and R. J. Hathaway, "Sequential Competitive Learning and the Fuzzy c-Means Clustering Algorithms," Neural Networks, vol.

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