Above are papers related to the following paper: [Paper_ID] Paper #154047. 	[Title] OPTIMAL ASYMPTOTIC IDENTIFICATION UNDER BOUNDED DISTURBANCES [Abstract] This paper investigates the intrinsic limitation of worst-case identification of LTI systems using data corrupted by bounded disturbances, when the unknown plant is known to belong to a given model set. This is done by analyzing the optimal worst-case asymptotic error achievable by performing experiments using any bounded inputs and estimating the plant using any identification algorithm. First, it is shown that under some topological conditions on the model set, there is an identification algorithm which is asymptotically optimal for any input. Characterization of the optimal asymptotic error as a function of the inputs is also obtained. These results hold for any error metric and disturbance norm. Second, these general results are applied to three specific identification problems: identification of stable systems in the ` 1 norm, identification of stable rational systems in the H 1 norm, and identification of unstable rational systems in the gap metric. For each of these problems, the general characterization of optimal asymptotic error is used to find near-optimal inputs to minimize the error. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112106. 	[Title] Worst-Case Identification of Nonlinear Fading Memory Systems [Abstract] In this paper, the problem of asymptotic identification for a class of fading memory systems in the presence of bounded noise is studied. For any experiment, the worst-case error is characterized in terms of the diameter of the worst-case uncertainty set. Optimal inputs that minimize the radius of uncertainty are studied and characterized. Finally, a convergent algorithm that does not require knowledge of the noise upper bound is furnished. The algorithm is based on interpolating data with spline functions, which are shown to be well suited for identification in the presence of bounded noise; more so than other basis functions such as polynomials. The methods as well as the results are quite general and are applicable to a larger variety of settings. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133846. 	[Title] Identification in H 1 with Nonuniformly Spaced Frequency Response Measurements [Abstract] In this paper, the problem of "system identification in H 1 " is investigated in the case when the given frequency response data is not necessarily on a uniformly spaced grid of frequencies. A large class of robustly convergent identification algorithms are derived. A particular algorithm is further examined and explicit worst case error bounds (in the H 1 norm) are derived for both discrete-time and continuous-time systems. Examples are provided to illustrate the application of the algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #154023. 	[Title] Robust Convergence of Two-Stage Nonlinear Algorithms for Identification in H 1 [Abstract]  Please summarise all the given information.

Paper #154047 has the title `OPTIMAL ASYMPTOTIC IDENTIFICATION UNDER BOUNDED DISTURBANCES  
` and the abstract `This paper investigates the intrinsic limitation of worst-case identification of LTI systems using data corrupted by bounded disturbances, when the unknown plant is known to belong to a given model set. This is done by analyzing the optimal worst-case asymptotic error achievable by performing experiments using any bounded inputs and estimating the plant using any identification algorithm. First, it is shown that under some topological conditions on the model set, there is an identification algorithm which is asymptotically optimal for any input. Characterization of the optimal asymptotic error as a function of the inputs is also obtained. These results hold for any error metric and disturbance norm. Second, these general results are applied to three specific identification problems: identification of stable systems in the ` 1 norm, identification of stable rational systems in the H 1 norm, and identification of unstable rational systems in the gap metric. For each of these problems, the general characterization of optimal asymptotic error is used to find near-optimal inputs to minimize the error. 
`.
Question: Which category should Paper #154047 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #154047 has the title `OPTIMAL ASYMPTOTIC IDENTIFICATION UNDER BOUNDED DISTURBANCES  
` and the abstract `This paper investigates the intrinsic limitation of worst-case identification of LTI systems using data corrupted by bounded disturbances, when the unknown plant is known to belong to a given model set. This is done by analyzing the optimal worst-case asymptotic error achievable by performing experiments using any bounded inputs and estimating the plant using any identification algorithm. First, it is shown that under some topological conditions on the model set, there is an identification algorithm which is asymptotically optimal for any input. Characterization of the optimal asymptotic error as a function of the inputs is also obtained. These results hold for any error metric and disturbance norm. Second, these general results are applied to three specific identification problems: identification of stable systems in the ` 1 norm, identification of stable rational systems in the H 1 norm, and identification of unstable rational systems in the gap metric. For each of these problems, the general characterization of optimal asymptotic error is used to find near-optimal inputs to minimize the error. 
`.
Question: Which category should Paper #154047 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks