THE PROBABILISTIC NEURAL NET NEURON’S NUMBER CALCULATIONS | Zendy

Galina Shcherbacova | Zendy; Victor Krylov | Zendy; Oleg Logvinov | Zendy

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THE PROBABILISTIC NEURAL NET NEURON’S NUMBER CALCULATIONS

Author(s) -

Galina Shcherbacova,

Victor Krylov,

Oleg Logvinov

Publication year - 2014

Publication title -

international journal of computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.184

H-Index - 11

eISSN - 2312-5381

pISSN - 1727-6209

DOI - 10.47839/ijc.11.2.559

Subject(s) - artificial neural network , probabilistic logic , computer science , noise (video) , stability (learning theory) , probabilistic neural network , layer (electronics) , compact space , algorithm , artificial intelligence , pattern recognition (psychology) , mathematics , time delay neural network , machine learning , mathematical analysis , organic chemistry , image (mathematics) , chemistry

The sub-gradient method of estimation of the number of the hidden layer neurons of a probabilistic neural network is suggested. This method allows evaluating the data compactness violation in λ-space. This evaluation based on the noise stability sub-gradient iterative optimization method. This method allows reducing the number of the hidden layer neurons and classification time.

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