Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The authors construct a class of elementary nonparametric output predictors of an unknown discrete-time nonlinear fading memory system. Their algorithms predict asymptotically ll for every bounded input sequence, every disturbance sequence in certain classes, and every linear or nonlinear system that is continuous and asymptotically time-invariant, causal, and with fading memory. The predictor is based on k/sub n/-nearest neighbor estimators from nonparametric statistics. It uses only previous input and noisy output data of the system without any knowledge of the structure of the unknown system, the bounds on the input, or the properties of noise. Under additional smoothness conditions the authors provide rates of convergence for the time-average errors of their scheme. Finally, they apply their results to the special case of stable linear time-invariant (LTI) systems.
['Sanjeev R. Kulkarni', 'S.E. Posner']
I am writing a research paper on a idea Nonparametric output prediction for nonlinear fading memory systems
[{"from": "human", "value": "I am writing a research paper on a idea Nonparametric output prediction for nonlinear fading memory systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The authors construct a class of elementary nonparametric output predictors of an unknown discrete-time nonlinear fading memory system. Their algorithms predict asymptotically ll for every bounded input sequence, every disturbance sequence in certain classes, and every linear or nonlinear system that is continuous and asymptotically time-invariant, causal, and with fading memory. The predictor is based on k/sub n/-nearest neighbor estimators from nonparametric statistics. It uses only previous input and noisy output data of the system without any knowledge of the structure of the unknown system, the bounds on the input, or the properties of noise. Under additional smoothness conditions the authors provide rates of convergence for the time-average errors of their scheme. Finally, they apply their results to the special case of stable linear time-invariant (LTI) systems."}]