Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of learning multiple continuous trajectories by means of recurrent neural networks with (in general) time-varying ights is addressed. The learning process is transformed into an optimal control framework where both the ights and the initial network state to be found are treated as controls. For such a task, a learning algorithm is proposed which is based on a variational formulation of Pontryagin's maximum principle. The convergence of this algorithm, under reasonable assumptions, is also investigated. Numerical examples of learning nontrivial two-class problems are presented which demonstrate the efficiency of the approach proposed.
['Lutz Leistritz', 'Miroslaw Galicki', 'Herbert Witte', 'Eberhard F. Kochs']
I am writing a research paper on a idea Initial state training procedure improves dynamic recurrent networks with time-dependent weights
[{"from": "human", "value": "I am writing a research paper on a idea Initial state training procedure improves dynamic recurrent networks with time-dependent weights"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of learning multiple continuous trajectories by means of recurrent neural networks with (in general) time-varying ights is addressed. The learning process is transformed into an optimal control framework where both the ights and the initial network state to be found are treated as controls. For such a task, a learning algorithm is proposed which is based on a variational formulation of Pontryagin's maximum principle. The convergence of this algorithm, under reasonable assumptions, is also investigated. Numerical examples of learning nontrivial two-class problems are presented which demonstrate the efficiency of the approach proposed."}]