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

作者：Haghighi E Bavafaye^*; Palm G; Rahmati M; Yazdanpanah M J

来源：Neural Networks, 2015, 65: 53-64.

DOI：10.1016/j.neunet.2015.01.010

• Multi-stable neural network
• Exponential number of attractors
• Lyapunov stability
• Classification
• Sinusoidal dynamic

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.

• 出版日期2015-5