neural network.
Dlya opisaniya algoritmov i ustroystv v neyroinformatike vÿrabotana
schodimosti on kompaktax. Proof Obobshchennaya
theorem Stouna ob
linear subsurface in C (X), 1 ÿ E, functions iz E razdelyayut tochki in X i E zamknuto
otnositelno nelineynoy unarnoy operatsii f ÿ C (R). Togda E = C (X). Nazvannÿe vÿshe
teoremy yavlyayutsya chastyu teoreticheskogo osnovaniya
zaimstvovanii gotovÿe metody preobrazovany tak, chtoby ix mojno bÿlo
i approximation of functional mnogich peremennyx superpositsiyami i
primeneniya neyronnyx setey dlya resheniya prikladnyx zadach, v tom chisle
podkhodÿ k resheniyu vspomogatelnyx zadach, okazavshixsya vajnymi dlya
For preobrazovaniya old and development of
new methods and algorithms
linear prostranstvam neprerÿvnyx functions, zamknutÿm otnositelno
1.3. Elements of artificial neural networks
universal approximation vozmojnostyax proizvolnoy
razdelax matematiki, sredi kotoryx metody optimizatsii, chislennÿe
and methods of training. The most common element of architecture
i sposoby sozdany dlya neyroinformatiki i v neyroinformatike. Pri
plotno v prostranstve vsex neprerÿvnyx funktsiy v topologii ravnomernoy
Obobshchennaya approximation theorem. Empty E ÿ C (X) - zamknutoe
neyrokompyuterov i neyroimitatorov, no
ochen udobny dlya opisaniya
approximation of functions, which obobshchaet i klassicheskuyu theorem Stone
dlya ispolzovaniya neyronnyx setey i pozvolyayut govorit o vozmojnosti
realizovat na vysokoparallelnyx kompyuternyx sistemax. Krome togo, as it was said
above, neuroinformatics offered some new
spetsialnaya "schemotexnika", in which elementary devices - summators, synapses,
neurons and t.p. obÿedinyayutsya in neyronnÿe seti, prednaznachennÿe dlya
resheniya razlichnyx zadach. Ispolzuemaya v
users.
lineynymi kombinatsiyami funktsyon odnoy peremennoy.
Ona otnositsya k
biological-ecological and ecological-medical.
lyuboy nelineynoy operatsii. The theorem is interpreted as confirmation of ob
Znachitelnaya chast apparata neyronnyx setey zaimstvovana v drugix
neyroinformatika ispolzuet spetsialnÿe sposoby opisaniya arhitektury
nelineynosti.
konstantÿ, lineynye funktsii i hotya by odnu nelineynuyu funktsiyu, to ono
methods, image recognition, mathematical statistics. Mnogie method
neyronnyx setey - synaps,
summator, nelineynyy element, tochka vetvleniya. These
elements are not necessarily used during construction
19
Machine Translated by Google
podklyuchenÿ, a vesa synapsov odnovremenno slujat vesami adaptivnogo
apparatnoy realizatsii vÿpolnennÿe na etom yazyke opisaniya perevodyatsya na
iz-za nalichiya vector nastraivaemyx parameters ÿ.
kvadratichnaya zavisimost ÿ prosteyshaya nelineynost, trebuyushchayasya dlya
neyronnyx setey vovse ne obyazatelno realizuyutsya kak otdelnÿe
chasti ili
vyxodnyx signals. Ee vychislenie takje mojno predstavit s pomoshchyu
neyronnÿe seti s kvadratichnymi summatorami imeyut bolee vÿsokuyu (v 2-4 raza)
skorost obucheniya i bolshuyu sposobnost k obobshcheniyu, odnako, slozhnost
realizatsii neyroimitatora vozrostaet mnogokratno.
nastraivaemÿy parameter, which
nazÿvaetsya ves synapse or
signal.
postoyannyy
edinichnyy signal. Here is the main - give on one of the entrances
signal
x on vector parameters ÿ. Drugimi slovami, on vÿchislyaet lineynuyu
obyazatelno. Summator s takim dopolnitelnÿm vxodom nazÿvayut
signal
x -
vxodnoy signal - i vÿdaet na vyxode proizvedenie
ÿx. Otdelno ot
form from the vector of the input signal X.
Exit signal raven ÿÿQijXiXj. At the size of the
input signal n kvadratichnyy summator imeet n (n + 1) / 2 nastraivaemyx parameters
Qij, a takje n + 1 obÿchnyx parametrov W. Summator etogo tipa xorosho issledovan v
rabotax. Dokazano, chto
for representation of neural networks and their observations. When programmnoy i
budem oboznachat ego tak, kak pokazano na ris. 2. Adaptivnym nazÿvaem ego
rassujdeniy bÿvaet udobno vydelit this element (Fig. 4). Kak pravilo, nabor sinapsov
rassmatrivaetsya vmeste s summatorom,
k kotoromu oni
yazÿki drugogo urovnya, bolee prigodnÿe dlya realizatsii. At this element
Dlya mnogix zadach polezno imet neodnorodnuyu lineynuyu funktsiyu
full class neyrosetevyx function. Experiments showed that
summatora. Vesa sets of synapses form a set of adaptive parameters,
Lineynaya svyaz - synaps
- est liniya peredachi signal, imeet odin
blocks.
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