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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Mishra, D., Tolambiya, A., Shukla, A., Kalra, P.K. (2006). Stability Analysis for Higher Order Complex-Valued Hopfield Neural Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_68
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DOI: https://doi.org/10.1007/11893028_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
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