## Abstract This paper will present a reduced input non linear state space model which describes the dynamic performance of a Proton Exchange Membrane airβhydrogen fuel cell (PEMFC). The mathematical model is based mainly on gases physical behaviour, which leads to reactants partial pressure evolut
Classification of Trends Via the Linear State Space Model
β Scribed by C. Gantert
- Publisher
- John Wiley and Sons
- Year
- 1994
- Tongue
- English
- Weight
- 666 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0323-3847
No coin nor oath required. For personal study only.
β¦ Synopsis
A method is presented for classification of trend curves based on the linear state space model. In this approach information about the smoothness of the trend curves is incorporated into the classification model by a nonstationary stochastic trend model and can thereby be used to obtain a better classification. In the case of small data sets the performance of the classification is significantly improved in comparison with the usual cluster analysis. Maximum likelihood estimation can be used to calculate the parameters of this model and to determine the classification. The classification algorithm is described in detail and the results are compared to those of the usual cluster analysis by simulation studies and by an application to tree ring data.
π SIMILAR VOLUMES
A linear space of order n is a pair (V, a), where V is a finite set of n elements and B is a set of subsets of V such that each 2-subset of V is contained in exactly one element of B. The exact number of nonisomorphic linear spaces was known up to order 10. Betten and Braun [l] found that there exis
## Abstract This paper describes a method for obtaining a time continuous reduced order model (ROM) from a system of time continuous linear differential equations. These equations are first put into a time discrete form using a finite difference approximation. The unit sample responses of the discr
It has been common practice to decompose an integrated time series into a random walk trend and a stationary cycle using the state space model. Application of state space trend-cycle decomposition, however, often results in a misleading interpretation of the model, especially when the observability