3,266 research outputs found
Two Identification Methods for Dual-Rate Sampled-Data Nonlinear Output-Error Systems
This paper presents two methods for dual-rate sampled-data nonlinear output-error systems. One
method is the missing output estimation based stochastic gradient identification algorithm and the other
method is the auxiliary model based stochastic gradient identification algorithm. Different from the
polynomial transformation based identification methods, the two methods in this paper can estimate
the unknown parameters directly. A numerical example is provided to confirm the effectiveness of the
proposed methods
Parameter and State Estimator for State Space Models
This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective
Gradient-based iterative parameter estimation for bilinear-in-parameter systems using the model decomposition technique
The parameter estimation issues of a block-oriented non-linear system that is bilinear in the parameters are studied, i.e. the bilinear-in-parameter system. Using the model decomposition technique, the bilinear-in-parameter model is decomposed into two fictitious submodels: one containing the unknown parameters in the non-linear block and the other containing the unknown parameters in the linear dynamic one and the noise model. Then a gradient-based iterative algorithm is proposed to estimate all the unknown parameters by formulating and minimising two criterion functions. The stochastic gradient algorithms are provided for comparison. The simulation results indicate that the proposed iterative algorithm can give higher parameter estimation accuracy than the stochastic gradient algorithms
Digits Recognition on Medical Device
With the rapid development of mobile health, mechanisms for automatic data input are becoming increasingly important for mobile health apps. In these apps, users are often required to input data frequently, especially numbers, from medical devices such as glucometers and blood pressure meters. However, these simple tasks are tedious and prone to error. Even though some Bluetooth devices can make those input operations easier, they are not popular enough due to being expensive and requiring complicated protocol support. Therefore, we propose an automatic procedure to recognize the digits on the screen of medical devices with smartphone cameras.
The whole procedure includes several βstandardβ components in computer vision: image enhancement, the region-of-interest detection, and text recognition. Previous works existed for each component, but they have various weaknesses that lead to a low recognition rate. We proposed several novel enhancements in each component.
Experiment results suggest that our enhanced procedure outperforms the procedure of applying optical character recognition directly from 6.2% to 62.1%. This procedure can be adopted (with human verification) to recognize the digits on the screen of medical devices with smartphone cameras
Iterative Solutions of a Set of Matrix Equations by Using the Hierarchical Identification Principle
This paper is concerned with iterative solution to a class of the real coupled matrix equations. By using the
hierarchical identification principle, a gradient-based iterative algorithm is constructed to solve the real coupled
matrix equations A1XB1+A2XB2=F1 and C1XD1+C2XD2=F2. The range of the convergence factor is derived to guarantee that the iterative algorithm is convergent for any initial value. The analysis indicates that
if the coupled matrix equations have a unique solution, then the iterative solution converges fast to the exact one
for any initial value under proper conditions. A numerical example is provided to illustrate the effectiveness of
the proposed algorithm
Multiple Adaptive System of Identification
It will be useful for students, postgraduates and doctoral research scholars who study the real objects.This scientific work aims to represent some elements of the theory of identification that are important for both practical use and further theoretical research in order to build logically complete basic and applied theory of identification as mathematically reasonable theory of knowledge of the cause-and-effect relationship in the objects of the real world.
For those specialists who carry out theoretical and experimental researches (technical, economic, biological, social etc) of the real-world objects with the aim of their optimal adaptive control, diagnostics of state, forecasting the consequences and so on
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