Fuzzy Logic and Fuzzy Sets


History

Lotfi Zadeh, at the University of California at Berkeley, first presented fuzzy logic in the mid-1960's.

Zadeh developed fuzzy logic as a way of processing data. Instead of requiring a data element to be either a member or non-member of a set, he introduced the idea of partial set membership.

In 1974 Mamdani and Assilian used fuzzy logic to regulate a steam engine.

In 1985 researchers at Bell laboratories developed the first fuzz ....etc

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DISTRIBUTED ALGORITHMS FOR CONSTRUCTING APPROXIMATE MINIMUM SPANNING TREES IN WIRELESS SENSOR NETWORKS --PARALLEL AND DISTRIBUTED SYSTEMS

While there are distributed algorithms for the minimum spanning tree (MST) problem, these algorithms require relatively large number of messages and time, and are fairly involved, making them impractical for resource-constrained networks such as wireless sensor networks. In such networks, a sensor has very limited power, and any algorithm needs to be simple, local, and energy efficient. Motivated by these ....etc

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Efficient Approximate Query Processing in Peer-to-Peer Networks

Abstract Peer-to-peer (P2P) databases are becoming prevalent on the Internet for distribution and sharing of documents, applications, and other digital media. The problem of answering large-scale ad hoc analysis queries, for example, aggregation queries, on these databases poses unique challenges. Exact solutions can be time consuming and difficult to implement, given the distributed and dynamic nature of P2P databases. In this paper, we present novel sampling-based techniques ....etc

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Knowledge Representation and Reasoning Logics for Arti cial Intelligence



Introduction
Propositional logics conceptualize domains at, but not below, the level of propositions.
A proposition is an expression in some language
that is true or false,
whose negation makes sense,
that can be believed or not,
whose negation can be believed or not,
that can be put in the frame
I believe that it is not the case that .
Some examples of propositions are
Betty ....etc

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Abstract
Based on the concept of incompatible factor, this
paper introduces further the principle of minimal
incompatibility, and use this principle to improve the
triple I method. In addition, by the principles of
maximal support and minimal incompatibility the
solutions of the problems of Fuzzy Modus Ponens and
Fuzzy Modus Tollens are unified. The reason why the
new method is more reasonable than the triple I
method is analyzed, and the new method is
generalized by considering two different types of
implication operators.
K ....etc

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Approximate Minimum Energy Multicasting in Wireless Ad-Hoc Networks (VC++)
IEE Transactions on Mobile Computing, Vol. 5,

Abstract:
A wireless ad hoc network consists of mobile nodes that are equipped with energy-limited batteries. As mobile nodes are battery-operated, an important issue in such a network is to minimize the total power consumption for each operation. Multicast is one of fundamental operations in any modern telecommunication network including wireless ad hoc networks. Given a multicast request consisting of a source ....etc

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Ocean of Reasoning



Translators Introduction
Homage to Man jus r in gratitude for his inspiration of the great teachers of
the Maha ya na tradition whose insight and care in composing their treatises
have made this work possible.
The reader of this translation of an early fifteenth-century Tibetan philosophical
text will have to get used to the heavily outlined prose style used by
Tsong khapa and other Tibetan scholars in the composition of such texts. So
let us get s ....etc

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Approximate dynamic programming with a fuzzy parameterization



Introduction
Dynamic programming (DP) is a powerful paradigm for solving
optimal control problems, thanks to its mild assumptions
on the controlled process, which can be nonlinear or stochastic
(Bertsekas, 2007; Bertsekas & Tsitsiklis, 1996). In the DP framework,
a model of the process is assumed to be available, and the
immediate performance is measured by a scalar reward signal. The
controller then maximizes t ....etc

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