About Model Complexity of 2-D Polynomial Discrete Systems: An Algebraic Approach

2014 ◽  
pp. 289-301
Author(s):  
Stelios Kotsios ◽  
Dionyssios Lappas
Keyword(s):  
Algebraic Approach ◽  
Discrete Systems ◽  
Model Complexity

An Algorithm for Obtaining Proper Models of Distributed and Discrete Systems

1995 ◽  
Vol 117 (4) ◽  
pp. 534-540 ◽  
Author(s):  
B. H. Wilson ◽  
J. L. Stein
Keyword(s):  
Spectral Radius ◽  
Discrete Systems ◽  
System Model ◽  
Model Complexity ◽  
Design Parameters ◽  
Automated Modeling ◽  
Modeling Software ◽  
Specific Frequency ◽  
Drive Systems ◽  
The Relationship

The development of automated modeling software requires strategies for synthesizing mathematical models of systems with distributed and discrete characteristics. A model order deduction algorithm (MODA) is developed to deduce a Proper System Model by selecting the proper complexity of submodels of components in a system subject to a frequency based metric. A Proper Model in this context means that (1) the system model has the minimum spectral radius out of all possible system models of equivalent or greater complexity, and (2) any increase in the model complexity will result in spectral radius beyond a specific frequency range of interest. Proper Models are also defined to have physically meaningful parameters. Proper Models are intended to be useful for design, where mapping the relationship between design parameters and dominant system dynamics is critical. While MODA is illustrated using the application of machine-tool drive systems, it is readily applicable to other modeling applications.

Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

2014 ◽  
Vol 61 (5) ◽  
pp. 402-415 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Model Complexity ◽  
Multinomial Model ◽  
Stochastic Dependence ◽  
New Model ◽  
Multinomial Processing Tree ◽  
Cognitive States ◽  
The Family ◽  
Tree Models ◽  
Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

Mean regulators for discrete systems

1992 ◽  
Vol 139 (1) ◽  
pp. 67 ◽  
Author(s):  
G.P. Liu
Keyword(s):  
Discrete Systems
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