Stability analysis for higher order complex-valued hopfield neural network

Authors:
Deepak Mishra;Arvind Tolambiya;Amit Shukla;Prem K. Kalra
Affiliations:
Department of Electrical Engineering, IIT Kanpur, India;Department of Electrical Engineering, IIT Kanpur, India;Department of Electrical Engineering, IIT Kanpur, India;Department of Electrical Engineering, IIT Kanpur, India
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

Citing 3
Cited 0

Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)

Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Complex-valued multistate neural associative memory

IEEE Transactions on Neural Networks
A new design method for the complex-valued multistate Hopfield associative memory

IEEE Transactions on Neural Networks

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Abstract

In this paper we consider a class of fully connected complex-valued neural networks which are a complex value extension of higher order real-valued Hopfield type neural networks. We proposed a energy function for higher order complex-valued Hopfield neural network and investigated the stability conditions. This proposed energy function formulation can be used for solving various problems such as optimization and synthesis of associative memory. In our work as an application, we discussed the recalling of a stored complex-valued vector (complex-valued associative memory). A real-valued approach is used for determining the weights and bias values for the proposed network.