This article reviews and investigates several basic recursive parameter identification
methods for a general stochastic system with colored noise (ie, output-error autoregressive …

Least squares is an important method for solving linear fitting problems and quadratic
optimization problems. This paper explores the properties of the least squares methods and …

Prediction based on time series has a wide range of applications. Due to the complex
nonlinear and random distribution of time series data, the performance of learning prediction …

With the development of advanced information and intelligence technologies, precision
agriculture has become an effective solution to monitor and prevent crop pests and …

Due to the nonlinear modeling capabilities, deep learning prediction networks have become
widely used for smart agriculture. Because the sensing data has noise and complex …

Compared with mechanism-based modeling methods, data-driven modeling based on big
data has become a popular research field in recent years because of its applicability …

Missing data often occur in industrial processes. In order to solve this problem, an auxiliary
model and a particle filter are adopted to estimate the missing outputs, and two unbiased …

For the lifted input–output representation of general dual-rate sampled-data systems, this
paper presents a decomposition based recursive least squares (D-LS) identification …

This paper considers the identification problems of the Hammerstein nonlinear systems. A
projection and a stochastic gradient (SG) identification algorithms are presented for the …

In this paper, the problems of parameter estimation are addressed for systems with scarce
measurements. A gradient-based algorithm is derived to estimate the parameters of the …