scholarly journals State Space Modeling with Non-Negativity Constraints Using Quadratic Forms

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1908
Author(s):  
Ourania Theodosiadou ◽  
George Tsaklidis
Keyword(s):  
State Space ◽  
Quadratic Forms ◽  
Hidden Variables ◽  
State Space Model ◽  
Estimation Procedure ◽  
Positive Component ◽  
Filtering Algorithm ◽  
Space Model ◽  
Space Modeling ◽  
The Difference

State space model representation is widely used for the estimation of nonobservable (hidden) random variables when noisy observations of the associated stochastic process are available. In case the state vector is subject to constraints, the standard Kalman filtering algorithm can no longer be used in the estimation procedure, since it assumes the linearity of the model. This kind of issue is considered in what follows for the case of hidden variables that have to be non-negative. This restriction, which is common in many real applications, can be faced by describing the dynamic system of the hidden variables through non-negative definite quadratic forms. Such a model could describe any process where a positive component represents “gain”, while the negative one represents “loss”; the observation is derived from the difference between the two components, which stands for the “surplus”. Here, a thorough analysis of the conditions that have to be satisfied regarding the existence of non-negative estimations of the hidden variables is presented via the use of the Karush–Kuhn–Tucker conditions.

scholarly journals Bayesian Analysis of Interleaved Learning and Response Bias in Behavioral Experiments

2007 ◽  
Vol 97 (3) ◽  
pp. 2516-2524 ◽  
Author(s):  
Anne C. Smith ◽  
Sylvia Wirth ◽  
Wendy A. Suzuki ◽  
Emery N. Brown
Keyword(s):  
Bayesian Analysis ◽  
State Space ◽  
State Space Model ◽  
Estimation Procedure ◽  
Ideal Observer ◽  
Model Parameters ◽  
Behavioral Experiments ◽  
Actual Object ◽  
Space Model ◽  
Multiple Tasks

Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood–based approaches in the analysis of a simulated conditioned T-maze task and of an actual object–place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.

scholarly journals Novel Method for State Space Modeling of Full Bridge Converter

2019 ◽  
Vol 8 (12) ◽  
pp. 4421-4426
Keyword(s):  
State Space ◽  
Power Conversion ◽  
State Space Model ◽  
Space Model ◽  
Space Modeling ◽  
Novel Method ◽  
Converter Design ◽  
Efficient Power ◽  
Wire Resistance ◽  
The Ideal

The requirement of converters is increasing with the increase in demand for the power conversion devices. For efficient power conversion, stability and time response of the system must be improved. For improving characteristics a mathematical model of the system must be determined. In this paper, an improvised state space model of full bridge converter is presented which can be used in converter design. This state-space model incorporates the non-idealities of the transformers like discharge time of primary inductance and secondary inductance as well as the wire resistance. The interdependency of the parameters affects the state space model of the converter compared to the ideal modeling. This variation in state space model of the converter has an impact on design of compensator which improves the system efficiency of the converter.

Responses comparison of the two discrete-time linear fractional state-space models

2016 ◽  
Vol 19 (4) ◽  
Author(s):  
Tadeusz Kaczorek ◽  
Piotr Ostalczyk
Keyword(s):  
State Space ◽  
State Space Model ◽  
State Space Models ◽  
Discrete State ◽  
Numerical Examples ◽  
Space Model ◽  
The Difference ◽  
Time Linear ◽  
Discrete State Space ◽  
Fundamental Matrices

AbstractIn this survey we consider two fractional-order discrete state-space models of linear systems. In both cases the crucial elements are the fundamental matrices. The difference between them is analyzed. A fundamental condition for the first state-space model is given. The investigations are illustrated by the numerical examples.

scholarly journals State Space Model with hidden variables for reconstruction of gene regulatory networks

2011 ◽  
Vol 5 (Suppl 3) ◽  
pp. S3 ◽  
Author(s):  
Xi Wu ◽  
Peng Li ◽  
Nan Wang ◽  
Ping Gong ◽  
Edward J Perkins ◽  
...  
Keyword(s):  
State Space ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Hidden Variables ◽  
State Space Model ◽  
Space Model ◽  
Gene Regulatory

Estimation of the clock TIE state space model using a thinning unbiased FIR filtering algorithm

2008 ◽  
Author(s):  
Yuriy S. Shmaliy ◽  
Oscar Ibarra-Manzano ◽  
Luis Arceo-Miquel ◽  
Luis Moralez-Mendoza ◽  
Oleksandr Yu. Shmaliy
Keyword(s):  
State Space ◽  
State Space Model ◽  
Filtering Algorithm ◽  
Space Model ◽  
Fir Filtering

scholarly journals Using a state-space model with hidden variables to infer transcription factor activities

2006 ◽  
Vol 22 (6) ◽  
pp. 747-754 ◽  
Author(s):  
Z. Li ◽  
S. M. Shaw ◽  
M. J. Yedwabnick ◽  
C. Chan
Keyword(s):  
Transcription Factor ◽  
State Space ◽  
Hidden Variables ◽  
State Space Model ◽  
Space Model

scholarly journals Intension, extension, and the model of belief and knowledge in economics

2012 ◽  
Vol 5 (2) ◽  
pp. 1 ◽  
Author(s):  
Ivan Moscati
Keyword(s):  
Economic Theory ◽  
State Space ◽  
State Space Model ◽  
Framing Effects ◽  
The State ◽  
Space Model ◽  
Mainstream Economics ◽  
The World ◽  
The Difference ◽  
Two Alternatives

This paper investigates a limitation of the model of belief and knowledge prevailing in mainstream economics, namely the state-space model. Because of its set-theoretic nature, this model has difficulties in capturing the difference between expressions that designate the same object but have different meanings, i.e., expressions with the same extension but different intensions. This limitation generates puzzling results concerning what individuals believe or know about the world as well as what individuals believe or know about what other individuals believe or know about the world. The paper connects these puzzling results to two issues that are relevant for economic theory beyond the state-space model, namely, framing effects and the distinction between the model-maker and agents that appear in the model. Finally, the paper discusses three possible solutions to the limitations of the state-space model, and concludes that the two alternatives that appear practicable also have significant drawbacks.

scholarly journals Representation of multi-connected system of Fuzzy State Space Modeling (FSSM) in potential method based on a network context

2017 ◽  
Vol 13 (4) ◽  
pp. 711-716 ◽  
Author(s):  
Jibril Aminu ◽  
Tahir Ahmad ◽  
Surajo Sulaiman
Keyword(s):  
State Space ◽  
State Space Model ◽  
Type A ◽  
Potential Method ◽  
Network System ◽  
Space Model ◽  
Space Modeling ◽  
State Space Modeling ◽  
Fuzzy State

The complexity of a system of Fuzzy State Space Modeling (FSSM) is the reason that leads to the main objective of this research. A multi-connected system of Fuzzy State Space Model is made up of several components, each of which performs a function. These components are interconnected in some manner and determine how the overall system operates. In this study, we study the concept of graph, network system and network projections which are the requisite knowledge to potential method. Finally, the multi-connected system of FSSM of type A namely feeder, common feeder and greatest common feeder are transformed into potential method using various method of transformation.

Sign in / Sign up

Export Citation Format

Share Document