Page 322 - Electrical Engineering Dictionary

generalization the process of inferring convert mechanical power into electrical
rules and taking decisions after a learning power, typically via Faraday induction ef-
phase has taken place. The process is sup- fects between moving and stationary current-
posed to take place on data not used during carrying coils and/or magnets. Electrostatic
learning. generators use mechanical motion to physi-
cally separate stationary charges to produce
generalized cone data structure for volu- a large electrostatic potential between two
metric representations, generated by sweep- electrodes.
ing an arbitrarily-shaped cross section along
a 3-D line called the “generalized cone axis.” generator coherency a group of gener-
ators where the rotor angles swing in syn-
generalized delta rule the weight update chronism with one another following a dis-
rule employed in the backpropagation algo- turbance. Usually, generators in close elec-
rithm. trical proximity and at some distance from
the fault tend to be coherent.
generalized Lloyd algorithm (GLA) a
generalization of the Lloyd (or Lloyd–Max) generator differential relay a genera-
algorithm for scalar quantizer design to op- tor differential relay is a differential relay
timal design of vector quantizers. See also specifically designed for protection of elec-
K-means algorithm. tric power generators. Variations include al-
lowances for split-phase winding machines.
generalized modus ponens generaliza-
tion of the classical modus ponens based on
generator inertia constant a term pro-
the compositional rule of inference.
portional to the combined moment of inertia
Let be a fuzzy rule A −→ B, that can
of the turbine-generator mass.
be interpreted as a fuzzy relation R, and a
0
fuzzy set A , then the compositional rule of
generator matrix a matrix used to de-
inference maps a fuzzy set
scribe the mapping from source word to code
word in a linear forward error control code.
0
0
0
B = A ◦ R = A ◦ (A −→ B)
The mapping is described through multipli-
cation of the source word by this matrix using
that can be interpreted as
element-wise finite field arithmetic. A linear
premise1: x is A 0 code is completely specified by its generator
(fact) matrix.
premise2 : if x is A then y is B
(fuzzy rule)
generator polynomial uniquely specifies
consequence : y is B 0 acycliccodeandhasdegreeequaltothenum-
(conclusion)
ber of the parity bits in the code. For an (n, k)
For example, if we have the fuzzy rule cyclic code, it is the only code word polyno-
“If the tomato is red then it is ripe,” and we mial of minimum degree (n − k).
know “The tomato is more or less red” (fact),
the generalized modus ponens can infer “The genetic algorithm an optimization tech-
tomato is more or less ripe.” nique that searches for parameter values by
See also compositional rule of inference, mimicking natural selection and the laws
fuzzy inference, fuzzy relation, linguistic of genetics. A genetic algorithm takes a
variable, modifier. set of solutions to a problem and measures
the “goodness” of those solutions. It then
generator in electrical systems, any of discards the “bad” solutions and keeps the
a variety of electromechanical devices that “good” solutions. Next, one or more genetic

c
2000 by CRC Press LLC