Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  construct generalized translation networks to uniformly approximate a class of nonlinear, continuous functionals defined on L p ([ 1,1] s ) for integer s 1, 1 p< 1 or C([1 ,1] s ).  obtain lor bounds on the possible order of approximation for such functionals in terms of any approximation process depending continuously upon a given number of parameters. Our networks almost achieve this order of approximation in terms of the number of parameters (neurons) involved in the network. The training is simple and noniterative; in particular,  avoid any optimization such as that involved in the usual back-propagation.
['H. N. Mhaskar', 'Nahmwoo Hahm']
I am writing a research paper on a idea Neural networks for functional approximation and system identification
[{"from": "human", "value": "I am writing a research paper on a idea Neural networks for functional approximation and system identification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  construct generalized translation networks to uniformly approximate a class of nonlinear, continuous functionals defined on L p ([ 1,1] s ) for integer s 1, 1 p< 1 or C([1 ,1] s ).  obtain lor bounds on the possible order of approximation for such functionals in terms of any approximation process depending continuously upon a given number of parameters. Our networks almost achieve this order of approximation in terms of the number of parameters (neurons) involved in the network. The training is simple and noniterative; in particular,  avoid any optimization such as that involved in the usual back-propagation."}]