A new class of multi-stable neural networks: Stability analysis and learning process

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...

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Bibliographic Details
Published in:Neural networks 2015-05, Vol.65, p.53-64
Main Authors: Bavafaye Haghighi, E., Palm, G., Rahmati, M., Yazdanpanah, M.J.
Format: Article
Language:English
Subjects:
Algorithms
Classification
Classification - methods
Classifiers
Dynamical systems
Dynamics
Exponential number of attractors
Learning
Lyapunov stability
Multi-stable neural network
Neural networks
Neural Networks (Computer)
Sinusoidal dynamic
Specifications
Stability
Topology
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2015.01.010