Abstract:
The main result in this paper is that for a neural circuit of the Hopfield type with a symmetric connection matrix T, the negative semidefiniteness of T is a necessary an...Show MoreMetadata
Abstract:
The main result in this paper is that for a neural circuit of the Hopfield type with a symmetric connection matrix T, the negative semidefiniteness of T is a necessary and sufficient condition for Absolute Stability. The most significant theoretical implication is that the class of neural circuits with a negative semidefinite T is the largest class of circuits that can be employed for embedding and solving optimization problems without the risk of spurious responses.<>
Published in: IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications ( Volume: 41, Issue: 7, July 1994)
DOI: 10.1109/81.298364
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- IEEE Keywords
- Index Terms
- Neural Network ,
- Absolute Stability ,
- Stability Of Neural Networks ,
- Sigmoid Function ,
- Smooth Function ,
- Input Vector ,
- Symmetric Matrix ,
- Equilibrium Point ,
- Global Stability ,
- Unique Point ,
- Maximum Slope ,
- Constant Vector ,
- Unique Equilibrium ,
- Solving Optimization Problems ,
- Class Of Matrices ,
- Negative Semi-definite ,
- Global Attractor ,
- Symmetric Connections
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Absolute Stability ,
- Stability Of Neural Networks ,
- Sigmoid Function ,
- Smooth Function ,
- Input Vector ,
- Symmetric Matrix ,
- Equilibrium Point ,
- Global Stability ,
- Unique Point ,
- Maximum Slope ,
- Constant Vector ,
- Unique Equilibrium ,
- Solving Optimization Problems ,
- Class Of Matrices ,
- Negative Semi-definite ,
- Global Attractor ,
- Symmetric Connections
