The contribution of this paper is two-fold. Firstly, the well-known two-point Interpolated Discrete Fourier Transform (IpDFT) algorithm based on a Maximum Sidelobe Decay (MSD) window is generalized to the estimation of the frequency and the ...

The first-order binomial autoregressive (BAR(1)) model is the most frequently used tool to analyze the bounded count time series. The BAR(1) model is stationary and assumes process parameters to remain constant throughout the time period, which ...

This paper deals with the determination of the optimal values to be given for the seven unknown parameters of the proton exchange membrane fuel cell (PEMFC). To this end, the weighted mean of vectors optimizer (INFO) metaheuristic algorithm is ...$$

Hydrodynamic models with rain-on-the-grid capabilities are usually computationally expensive for automatic parameter estimation. In this paper, we present a global optimization-based algorithm to calibrate a fully distributed hydrologic-...

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• An automatic calibration algorithm for distributed flood and water quality modeling is developed.
• It uses HydroPol2D model and calibrate water quantity and quality parameters globally.
• Data from observed gauges such as discharges, ...

Saturation nonlinearity exists widely in various practical control systems. Modeling and parameter estimation of systems with saturation nonlinearity are of great importance for analyzing their characteristics and controller design. This paper ...

It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of ...

Knowledge-based leader-following synchronization of heterogeneous nonlinear multi-agent systems is a challenging problem since the leader’s dynamic information is unknown to any follower node. This paper proposes a learning-based fully ...

System identification determines the model of the plant from the measurement data and has been widely used in the prediction and control. In this paper, the parameter estimation problem is studied for multivariate equation-error systems with ...

• A fourth-order polynomial phase signal model is used to model the echo, and a novel parameter estimation method is proposed.
• A frequency domain implementation method of SoWVD is proposed, ...

The high maneuvering target with jerk motion detection suffers from range migration (RM) and Doppler frequency migration (DFM). In this paper, the fourth order polynomial signal model is used to model the radar echo signal, and a novel high ...

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations which operate in different spectra. Due ...

This paper mainly studies the parameter identification problem of multiple-input and single-output Hammerstein controlled autoregressive moving average (MISO-HCARMA) system in multivariable systems. A parameter identification method based on ...

Fractional uncertain differential equations are used for forecast in this paper. It is crucial to provide an accurate estimation method since fractional uncertain differential equations hold memory effects and parameter estimation strongly depends ...${}_{\mathrm{}}$

Stock markets around the world are growing rapidly, and vulnerability created by default risk of option holders has become a serious issue. In such circumstances, this paper suggests the pricing of vulnerable European and barrier options, when the ...

The Support Vector Regression (SVR) technique can approximate intricate systems by addressing learning and estimation challenges within a reproducing kernel Hilbert space, devoid of reliance on specific parameter assumptions. However, when ...

• An augmented support vector regression model with an autoregressive process is designed to model temporal data.
• A robust iterative procedure is developed for training the proposed SVR.
• The working likelihood method is used for ...

In this paper, we introduce a new class of asymmetric beta-binomial generalized autoregressive conditional heteroscedastic (GARCH) models for bounded integer-valued time series, which can capture the asymmetric impact of positive and negative ...

• A new asymmetric beta-binomial GARCH model for bounded Z-valued time series.
• Asymmetric impact of positive and negative observations.
• CML estimation with asymptotic properties.
• A real time series data in the field of ...

The photovoltaic system has been widely integrated into electrical power grids to produce clean and sustainable energy sources. Precisely modeling of PV systems is crucial to simulate and asset the performance of such power system. Modeling of PV ...

An estimation law of unknown parameters vector θ is proposed for one class of nonlinearly parameterized regression equations y t = Ω t Θ θ. We restrict our attention to parameterizations that are widely obtained in practical scenarios when ...

For the problem of time-varying parameter estimation and its convergence, this paper proposes an invariant matrix to represent the changing laws of time-varying parameters, which suggests a novel expression to describe the parameters by employing ...

This paper presents a hierarchical identification model for multiple-input multiple-output (MIMO) systems. An auxiliary model-based hierarchical stochastic gradient (AM-HSG) algorithm is derived by means of the auxiliary model identification idea ...

Pushing is an essential non-prehensile manipulation skill used for tasks ranging from pre-grasp manipulation to scene rearrangement, reasoning about object relations in the scene, and thus pushing actions have been widely studied in robotics. The ...

• Graph neural network based framework for parameter estimation and physics prediction.
• Articulation based graph representation for modeling multi-part objects.
• 6D Effect Prediction.
• Tool Manipulation Planning.
• Model ...