Learning Algorithm with Nonlinear Map Optimization for Neural Network

• Published in: Journal of Signal Processing Vol. 22; no. 4; pp. 153 - 156
• Main Authors: Jin'no, Kenya, Hoshino, Yuki
• Format: Journal Article
• Language: English
• Published: Tokyo Research Institute of Signal Processing, Japan 25.07.2018; Japan Science and Technology Agency
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Computation; Computer simulation; Machine learning; Neural networks; Optimization; Swarm intelligence; Teaching methods
• ISSN: 1342-6230; 1880-1013; 1880-1013
• DOI: 10.2299/jsp.22.153

Recently, machine learning has been attracting attention. Machine learning is mainly realized by the learning of artificial neural networks. Various learning methods have been proposed; however, the learning methods are based on gradient methods. On the other hand, swarm intelligence (SI) algorithms have been attracting attention in the optimization field. Generally speaking, SI algorithms have a large computation cost. Therefore, there are few cases of SI algorithms being applied to machine learning. In this paper, we propose a novel learning algorithm for an artificial neural network which applies our proposed nonlinear map optimization (NMO) method. NMO consists of some simple particles which are driven by a simple nonlinear map. NMO can be classified as an SI algorithm. However, it has only a small computation cost. Therefore, NMO can be applied to a learning algorithm for an artificial neural network. In this paper, we introduce NMO, and a small learning simulation is carried out to confirm the performance of our learning method.