Fredholm integral relation between compound estimation and prediction (FIRCEP)

Stehlík, Milan; Kiseľák, Jozef; Bukina, Elena; Lü, Ying; Baran, Sándor

doi:10.1080/07362994.2019.1696211

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Such ill posed information relationships can be formulated in the form of 1-st kind Fredholm equations. Under reasonable regularities and simplicity of the underling process, e.g., autoregressive statistical models, one can apply FIRCEP methodology [5] to obtain such relationships. Such kind of ill-posed information relationships can be formulated also from the perspective of information divergences, and ill-posedness can be translated to normal language as a not-avoidable imprecision of any model with respect to underlying parameter estimation/prediction.…”

Section: Introductionmentioning

confidence: 99%

On COVID-19 outbreaks predictions: Issues on stability, parameter sensitivity, and precision

Stehlík

Kiseľák

Dinamarca

et al. 2020

Stochastic Analysis and Applications

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We formulate ill-posedness of inverse problems of estimation and prediction of Coronavirus Disease 2019 (COVID-19) outbreaks from statistical and mathematical perspectives. This is by nature a stochastic problem, since e.g., random perturbation in parameters can cause instability of estimation and prediction. This leaves us with a plenty of possible statistical regularizations, thus generating a plethora of sub-problems. We can mention as examples stability and sensitivity of peak estimation, starting point of exponential growth curve, or estimation of parameters of SIR (Susceptible-Infected-Removed) model. Moreover, each parameter has its specific sensitivity, and naturally, the most sensitive parameter deserves a special attention. E.g., in SIR model, parameter b is more sensitive than parameter c. In a simple exponential epidemic growth model, parameter b is more sensitive than the parameter a. We also discuss on statistical quality of COVID-19 incidence prediction, where we justify an exponential curve considering the microbial growth in ideal conditions for epidemic. The empirical data from Iowa State, USA, Hubei Province in China, New York State, USA, and Chile justifies an exponential growth curve for initiation of epidemics outbreaks.

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Section: Introductionmentioning

confidence: 99%

On COVID-19 outbreaks predictions: Issues on stability, parameter sensitivity, and precision

Stehlík

Kiseľák

Dinamarca

et al. 2020

Stochastic Analysis and Applications

Self Cite

View full text Add to dashboard Cite

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The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks

Mansouri

Kaddar

Benkabou

et al. 2020

Neural Comput & Applic

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A novel adaptive control design method for stochastic nonlinear systems using neural network

Aghajary

Gharehbaghi

2021

Neural Comput & Applic

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This paper presents a novel method for designing an adaptive control system using radial basis function neural network. The method is capable of dealing with nonlinear stochastic systems in strict-feedback form with any unknown dynamics. The proposed neural network allows the method not only to approximate any unknown dynamic of stochastic nonlinear systems, but also to compensate actuator nonlinearity. By employing dynamic surface control method, a common problem that intrinsically exists in the back-stepping design, called “explosion of complexity”, is resolved. The proposed method is applied to the control systems comprising various types of the actuator nonlinearities such as Prandtl–Ishlinskii (PI) hysteresis, and dead-zone nonlinearity. The performance of the proposed method is compared to two different baseline methods: a direct form of backstepping method, and an adaptation of the proposed method, named APIC-DSC, in which the neural network is not contributed in compensating the actuator nonlinearity. It is observed that the proposed method improves the failure-free tracking performance in terms of the Integrated Mean Square Error (IMSE) by 25%/11% as compared to the backstepping/APIC-DSC method. This depression in IMSE is further improved by 76%/38% and 32%/49%, when it comes with the actuator nonlinearity of PI hysteresis and dead-zone, respectively. The proposed method also demands shorter adaptation period compared with the baseline methods.

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Fredholm integral relation between compound estimation and prediction (FIRCEP)

Cited by 3 publications

References 28 publications

On COVID-19 outbreaks predictions: Issues on stability, parameter sensitivity, and precision

On COVID-19 outbreaks predictions: Issues on stability, parameter sensitivity, and precision

The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks

A novel adaptive control design method for stochastic nonlinear systems using neural network

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