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24 ПродажO’qituvchi bilan o’rganish, xatolarni teskari tarqatish algaritmi
![ikkinchi kategoriya ma’lumotlari esa tо‘rni testlash uchun ishlatiladi. Neyron
tо‘rining haqiqiy sifati faqat tо‘rni testlash vaqtida namoyon bо‘ladi, tо‘rni
о‘qitishning muvaffaqiyatli tugaganligi testlash jarayonida notо‘g‘ri ishlash
belgilarining yо‘qligidadir. Testlash jarayoni shunday tashkil qilish kerakki, tо‘rni
ishlash jarayonida olingan bilimlarni umumlashtirish qobiliyati baholananishi
zarur. Bu yerda bilimlarni umumlashtirish deganda berilgan ma’lumotlar bо‘yicha
masalani tо‘g‘ri yechimini ishlab chiqish qobiliyatiga aytiladi.
Neyron tо‘rini о‘qitishni oddiy misolda kо‘rib chiqamiz. Aytaylik neyron
tо‘ri bitta kirish va bitta chiqish elementidan tarkib topgan. Maktab kursidan
ma’lumki fizika darslarida tajribalar о‘tkaziladi va dekart tekisligida uning grafigi
quriladi. Masalan, quyidagi tajriba ma’lumotlari olingan: kirish signallarini qiymati
X={x
i }, Y={y
i }, i=1,2,….,n. Grafikda korrelyatsiya maydonidagi nuqtalarning
joylashganligiga qarab bu yerdagi kirish va chiqish orasidagi analitik bog‘lanish
tо‘g‘ri chiziqga yaqinligshi kо‘rinib turibdi [44-48].
Statistik tahlildagi eng kichik kvadratlar metodini qо‘llab izlanayotgan
tо‘g‘ri chiziqning parametrlarini topish mumkin. Tо‘g‘ri chiziq tenglamasi
quyidagi formula bilan beriladi
Y = ax + b
,
bu yerda x va u о‘zgaruvchilar (masalan, о‘rganilayotgan jarayondagi biror
parametrning qiymatlari), a tо‘g‘ri chiziqning x о‘qiga nisbatan qiyshayishini yoki
gradiyentini aniqlaydi, b esa tо‘g‘ri chiziqni u о‘qini kesish nuqtasi. Eng kichik
kvadratlar usuli bilan analitik funksiyaning a va b parametrlarini qiymatini
aniqlashimiz mumkin.
Demak, maqsadimiz, nuqtalar orasidan shunday tо‘g‘ri chiziqni о‘tkazish
kerakki, unda tо‘g‘ri chiziq bilan nuqtalar orasidagi farqlarning kvadratlarini
umumiy yig‘indisi eng kichik bо‘lsin.
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![FOYDALANILGAN ADABIYOTLAR
1. Cybenko, G. 1989. Approximation by superpositions of a sigmoidal
function Mathematics of Control, Signals, and Systems, 2(4), 303–314.
2. Linnainmaa, Seppo (1970). The representation of the cumulative
rounding error of an algorithm as a Taylor expansion of the local rounding errors
(Masters) (in Finnish). University of Helsinki. pp. 6–7.
3. Kelley, Henry J. (1960). "Gradient theory of optimal flight paths".
ARS Journal. 30 (10): 947–954. doi:10.2514/8.5282.
4. Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and
the Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961
5. Werbos, Paul (1982). "Applications of advances in nonlinear
sensitivity analysis" (PDF). System modeling and optimization. Springer. pp. 762–
770. Archived (PDF) from the original on 14 April 2016. Retrieved 2 July 2017.
6. Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams.
"Learning Internal Representations by Error Propagation". David E. Rumelhart,
James L. McClelland, and the PDP research group. (editors), Parallel distributed
processing: Explorations in the microstructure of cognition, Volume 1:
Foundation. MIT Press, 1986.
7. Hastie, Trevor. Tibshirani, Robert. Friedman, Jerome. The Elements
of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New
York, NY, 2009.
8. Rosenblatt, Frank (1958). "The Perceptron: A Probabilistic Model For
Information Storage And Organization in the Brain". Psychological Review. 65
(6): 386–408. CiteSeerX 10.1.1.588.3775. doi:10.1037/h0042519. PMID
13602029. S2CID 12781225.
9. Rosenblatt, Frank (1962). Principles of Neurodynamics. Spartan, New
York.
10. Schmidhuber, Juergen (2022). "Annotated History of Modern AI and
Deep Learning". arXiv:2212.11279 [cs.NE].
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O’QITUVCHI BILAN O’RGANISH, XATOLARNI TESKARI TARQATISH ALGARITMI..docx