A new class of multi-stable neural networks: Stability analysis and learning process. (May 2015)

• Record Type: Journal Article
• Authors: Bavafaye Haghighi, E.; Palm, G.; Rahmati, M.; Yazdanpanah, M.J.
• Is Part Of: Neural networks. Volume 65(2015:May)
• Page Start: 53
• Page End: 64
• Publication Date: 2015-05
• Subjects: Multi-stable neural network -- Exponential number of attractors -- Lyapunov stability -- Classification -- Sinusoidal dynamic; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.01.010
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Recently, multi-stable Neural Networks (NN) with exponential number of attractors have been presented and analyzed theoretically; however, the learning process of the parameters of these systems while considering stability conditions and specifications of real world problems has not been studied. In this paper, a new class of multi-stable NNs using sinusoidal dynamics with exponential number of attractors is introduced. The sufficient conditions for multi-stability of the proposed system are posed using Lyapunov theorem. In comparison to the other methods in this class of multi-stable NNs, the proposed method is used as a classifier by applying a learning process with respect to the topological information of data and conditions of Lyapunov multi-stability. The proposed NN is applied on both synthetic and real world datasets with an accuracy comparable to classical classifiers.