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 Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Gergely Takács ◽  
Tomáš Polóni ◽  
Boris Rohal’-Ilkiv
Keyword(s):  
Control System ◽  
Vibration Control ◽  
State Space ◽  
Real Time ◽  
Cantilever Beam ◽  
State Space Model ◽  
Model Parameters ◽  
Extended Kalman Filtering ◽  
Space Model ◽  
Continuous State

This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

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.

A BAYESIAN ANALYSIS OF WEAK IDENTIFICATION IN STOCK PRICE DECOMPOSITIONS

2014 ◽  
Vol 19 (4) ◽  
pp. 728-752 ◽  
Author(s):  
Nathan S. Balke ◽  
Jun Ma ◽  
Mark E. Wohar
Keyword(s):  
Bayesian Analysis ◽  
State Space ◽  
Bayesian Methods ◽  
Stock Price ◽  
State Space Model ◽  
Expected Returns ◽  
Fundamental Issue ◽  
Weak Identification ◽  
Space Model ◽  
Substantial Uncertainty

This paper employs the state-space model to reexamine the fundamental issue in finance of whether it is the expected returns or the expected dividends growth that is primarily responsible for stock price variations. We use Bayesian methods to show that there is a substantial uncertainty about the contributions of expected returns and expected dividends to fluctuations in the price–dividend ratio when the aggregate returns and dividends data are used. The substantial uncertainty of the contributions results from the model being weakly identified. Our finding challenges the notion long held in the existing literature that it is the expected returns that contribute most to price–dividend variations.

Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams

2021 ◽  
pp. 014233122110183
Author(s):  
Shaowei Wang ◽  
Cong Xu ◽  
Chongshi Gu ◽  
Huaizhi Su ◽  
Bangbin Wu
Keyword(s):  
State Space ◽  
Elastic Deformation ◽  
State Space Model ◽  
State Equation ◽  
Time Effect ◽  
The State ◽  
Arch Dam ◽  
Model Parameters ◽  
Concrete Dams ◽  
Space Model

Displacement is the most intuitive reflection of the comprehensive behavior of concrete dams, especially the time effect displacement, which is a key index for the evaluation of the structural behavior and health status of a dam in long-term service. The main purpose of this paper is to establish a state space model for separating causal components from the measured dam displacement. This approach is conducted by initially proposing two equations, which are the state and observation equations, and model parameters are then optimized by the Kalman filter algorithm. The state equation is derived according to the creep deformation of dam concrete and foundation rock and is used to preliminarily predict the dam time effect displacement. Considering the generally recognized three components of dam displacement, the hydraulic-seasonal-time (HST) model is used to establish the observation equation, which is used to update the time effect displacement. The efficiency and rationality of the established state space model is verified by an engineering example. The results show that the hydraulic component separated by the state space model only contains the instantaneous elastic hydraulic deformation, while the hysteretic elastic hydraulic deformation is divided into the time effect component. The inverted elastic modulus of dam body concrete is an instantaneous value for the state space model but a comprehensive reflection of the instantaneous and hysteretic elastic deformation ability for the HST model, where the hysteretic elastic deformation is a part of the hydraulic component. For the Xiaowan arch dam, the inverted values are 42.9 and 36.7 GPa for the state space model and HST model, respectively. The proposed state space model is useful to improve the interpretation ability of the separated displacement components of concrete dams.

scholarly journals On Sequential Learning for Parameter Estimation in Particle Algorithms for State-Space Models

2016 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Chunlin Ji
Keyword(s):  
State Space ◽  
Blind Deconvolution ◽  
State Space Model ◽  
The State ◽  
State Space Models ◽  
Sequential Learning ◽  
Model Parameters ◽  
Learning Method ◽  
Space Model ◽  
Deconvolution Problem

Particle methods, also known as Sequential Monte Carlo, have been ubiquitous for Bayesian inference for state-space models, particulary when dealing with nonlinear non-Gaussian scenarios. However, in many practical situations, the state-space model contains unknown model parameters that need to be estimated simultaneously with the state. In this paper, We discuss a sequential analysis for combined parameter and state estimation. An online learning method is proposed to approach the distribution of the model parameter by tuning a flexible proposal mixture distribution to minimize their Kullback-Leibler divergence. We derive the sequential learning method by using a truncated Dirichlet processes normal mixture and present a general algorithm under a framework of the auxiliary particle filtering. The proposed algorithm is verified in a blind deconvolution problem, which is a typical state-space model with unknown model parameters. Furthermore, in a more challenging application that we call meta-modulation, which is a more complex blind deconvolution problem with sophisticated system evolution equations, the proposed method performs satisfactorily and achieves an exciting result for high efficiency communication.

H∞-norm-based optimization for the identification of gray-box LTI state-space model parameters

2016 ◽  
Vol 92 ◽  
pp. 34-41 ◽  
Author(s):  
Daniel Vizer ◽  
Guillaume Mercère ◽  
Olivier Prot ◽  
Edouard Laroche
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Parameters ◽  
Space Model

Online remaining useful life prognostics using an integrated particle filter

2018 ◽  
Vol 232 (6) ◽  
pp. 587-597 ◽  
Author(s):  
Yawei Hu ◽  
Shujie Liu ◽  
Huitian Lu ◽  
Hongchao Zhang
Keyword(s):  
Particle Filter ◽  
State Space ◽  
State Space Model ◽  
Remaining Useful Life ◽  
Recursive Estimation ◽  
Model Parameters ◽  
Mechanical Equipment ◽  
Milling Cutter ◽  
Space Model ◽  
Useful Life

The lifetime evolution of mechanical equipment with complicated structure and the harsh operating environment cannot be accurately expressed due to the dynamics of the failure mechanism. However, the performance monitoring of equipment, with the information characterizing the failure process from the sensed data, can be used to assess the failure time and then the online remaining useful life. Because of the existence of nonlinearity and non-Gaussian for most real systems, for online assessment, unscented Kalman filter combined with particle filter is studied, instead of the standard particle filter with importance sampling, which is modified to update the states iteratively. Meanwhile, Markov chain Monte Carlo is performed after resampling to improve the prediction accuracy. In the modeling, state–space model is developed to quantify the relationship between the information from online observation and underlying degradation, and the unscented particle filter is investigated to realize the assessment of remaining useful life. In particular, the sufficient statistic method is presented to obtain a joint recursive estimation on both the system state and model parameters for those state–space model with unknown time-invariant ones. At the end of this article, the acoustic emission signals of a milling cutter are illustrated as a case study for cutter online remaining useful life estimate. The milling cutter example demonstrates the effectiveness of the proposed method for online estimate and provides useful insights regarding the necessity of online updating and the assessment.

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