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- W2165721787 abstract "Neural networks are often criticised for being black-boxes, in that their internal behaviour is hard to understand. In this paper, it is shown how the internal behaviour of trained Hopfield neural networks can be better understood, through analysis from a control theory viewpoint. The required techniques from control theory and differential geometry are presented. An explanation of why such analysis is important is provided, along with details of how nonlinear controllers can be produced using such analysis. The paper is concluded with an example, in which the internal behaviour of a trained Hopfield network is analysed using the techniques described." @default.
- W2165721787 created "2016-06-24" @default.
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- W2165721787 date "1999-01-01" @default.
- W2165721787 modified "2023-09-24" @default.
- W2165721787 title "Trained Hopfield neural networks need not be black-boxes" @default.
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- W2165721787 doi "https://doi.org/10.1109/acc.1999.782803" @default.
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