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Нейронные сети . PowerPoint PPT Presentation


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Нейронные сети. Модель нейрона. Represents a neuron in the brain. X P. X 2. X 1. S is a function on the interval (0,1) representing the strength of the output. Activation Function. O=b i0 + b i1 X 1 + … + b ip X p. s. 1. 0. s(O). O. C труктура сети. P predictors (inputs). X 1.

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Нейронные сети .

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.


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Represents a neuron in the brain

XP

X2

...

X1

S is a function on the interval (0,1) representing the strength of the output

Activation Function

O=bi0 + bi1X1 + + bipXp

s

1

0

s(O)

O


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C .

P predictors (inputs)

X1

X2

XP

. . .

1 Hidden Layer with M Neurons

M

1

2

. . .

1 output

Y


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.

. { xs,ds}, s=1m . ds . ys ds .

E=sE=(1/2)s(dsj ysj)2, dsj, ysj j . Es , . : 1) ; 2) ; 3) s; 4) (, , ) , ; 5) 14 , . , .


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, (JET).


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n/ .


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n ( ) ( ) . , .


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n

, , t TOFtIRT


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n Q1Q2, .


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n QtIRT .


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Q1Q2 lin/quad

QIRT lin/quad

NN

. (keV ee ).


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, n/ feed-forward 71 (~ 200 ), 20 5 .

NN , : NN~5% 20 ~20 25 %, . 200 1 2 %.This methodcan be implemented into the most modern programmable ADC cards und used for real time application in most cases. This is due to fast application time of NN which is usually in the range of a few s.


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/0, feed-forward NN with backpropagation algorithm

e()/hadron separation


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/sep


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ZEUSa (LHC). ( ).


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DELPHI RICH dE/dx Time Projection Chamber (TPC).

, MAGRIB, RICH dE/dx NN . NN , 16 , , 19 . RICH TPC , .


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NN

RIBMEAN

- (NN) (RIBEAN). NN- 56% , purity 16%. purity .


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NN

HADSIN

. purity 4-.


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f=(Erec-E)/E^0.5

<f>=-0.71

/E=1.1/E 1/2

<f>=-0.018

/E=.75/E 1/2

f, DELPHI . . f, , NN : feed-forward (33, 53,1) . RPROP . NN- ~ 1.5 .


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Tevatron/LHC parameters

DELPHI/ CMS parameters


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: (LHC), , , HEP onlineoffline. , . NN , . . NN HEP ( , , , ).


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Application of Neural Networks Optimized by Genetic Algorithms to Higgs Boson Search

Frantiek Hakl, Marek Hlavaek, and Roman Kalous (Prague, Czech Republic)

An application of a neural network approach to Higgs search in the assosiated production t t H with H->b b. This decay channel is considered as a discovery channel for Higgs scenarios for Higgs boson masses in the range 80 130 GeV. Our results show that NN approach is applicable to the problem of Higgs boson detection.


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Reinhard SchwienhorstMICHGAN STATE UNIVERSITYCPPM D0 Seminnar, June 20 2008 Advanced event analysis methods


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: , , , , , . (), . ( ), ( ). , , . , , , , . unheimlich - , , . . . : Das ist nicht Mathematik/ Das ist Theologie.


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