- Tytuł:
- Convergence Analysis of Inverse Iterative Neural Networks with L₂ Penalty
- Autorzy:
-
Wen, Y.
Wang, J.
Huang, B.
Zurada, J. M. - Powiązania:
- https://bibliotekanauki.pl/articles/108754.pdf
- Data publikacji:
- 2016
- Wydawca:
- Społeczna Akademia Nauk w Łodzi
- Tematy:
-
neural networks
gradient descent
inverse iterative
monotonicity
regularization
convergence - Opis:
- The iterative inversion of neural networks has been used in solving problems of adaptive control due to its good performance of information processing. In this paper an iterative inversion neural network with L₂ penalty term has been presented trained by using the classical gradient descent method. We mainly focus on the theoretical analysis of this proposed algorithm such as monotonicity of error function, boundedness of input sequences and weak (strong) convergence behavior. For bounded property of inputs, we rigorously proved that the feasible solutions of input are restricted in a measurable field. The weak convergence means that the gradient of error function with respect to input tends to zero as the iterations go to infinity while the strong convergence stands for the iterative sequence of input vectors convergence to a fixed optimal point.
- Źródło:
-
Journal of Applied Computer Science Methods; 2016, 8 No. 2; 85-98
1689-9636 - Pojawia się w:
- Journal of Applied Computer Science Methods
- Dostawca treści:
- Biblioteka Nauki
Artykuł