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- Title
Determining the Number of Neurons in Artificial Neural Networks for Approximation, Trained with Algorithms Using the Jacobi Matrix.
- Authors
Yotov, Kostadin; Hadzhikolev, Emil; Hadzhikoleva, Stanka
- Abstract
How can we determine the optimal number of neurons when constructing an artificial neural network? This is one of the most frequently asked questions when working with this type of artificial intelligence. Experience has brought the understanding that it takes an individual approach for each task to specify the number of neurons. Our method is based on the requirement of algorithms looking for a minimum of functions of type S(z) = ∑mi=1 [φi(z)]² that satisfy the inequality p ≤ m, where p is the dimensionality of the argument z, and m is the number of functions. Formulas for an upper limit of the required neurons are proposed for networks with one hidden layer and for networks with r hidden layers with an equal number of neurons.
- Subjects
ARTIFICIAL neural networks; JACOBI operators; NEURONS; SCHUR functions; ARTIFICIAL intelligence
- Publication
TEM Journal, 2020, Vol 9, Issue 4, p1320
- ISSN
2217-8309
- Publication type
Article
- DOI
10.18421/TEM94-02