Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: New learning rules for computing eigenspaces and eigenvectors for symmetric and nonsymmetric matrices are proposed. By applying Liapunov stability theory, these systems are shown to be globally convergent. Properties of limiting solutions of the systems and ighted versions are also examined. The proposed systems may be vied as generalizations of Oja's and Xu's principal subspace learning rules. Numerical examples showing the convergence behavior are also presented.
['Mohammed A. Hasan']
I am writing a research paper on a idea Generalizations of Oja's Learning Rule to Non-Symmetric Matrices
[{"from": "human", "value": "I am writing a research paper on a idea Generalizations of Oja's Learning Rule to Non-Symmetric Matrices"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: New learning rules for computing eigenspaces and eigenvectors for symmetric and nonsymmetric matrices are proposed. By applying Liapunov stability theory, these systems are shown to be globally convergent. Properties of limiting solutions of the systems and ighted versions are also examined. The proposed systems may be vied as generalizations of Oja's and Xu's principal subspace learning rules. Numerical examples showing the convergence behavior are also presented."}]