The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems. The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations. In addition, the models are tested for nonlinear systems with different noise conditions. In a nutshell, the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies.,Population-based evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and cat swarm optimization (CSO) are implemented in a distributed form to address the system identification problem having distributed input data. Out of different distributed approaches mentioned in the literature, the study has considered incremental and diffusion strategies.,Performances of the proposed distributed learning-based algorithms are compared for different noise conditions. The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate, but incremental CSO is slightly superior to diffusion CSO.,This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems. Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.

On the development of cat swarm metaheuristic using distributed learning strategies and the applications

This paper studies one identification problem for a piecewise affine system which is a special nonlinear system. As the difficulty in identifying the piecewise affine system is to determine each se...

https://www.tandfonline.com/doi/pdf/10.1080/21642583.2020.1737845

Zonotope parameter identification for piecewise affine system

/pdf/zonotope-parameter-identification-for-piecewise-affine-tb5v1i3cf0.pdf

Zonotope Parameter Identification for Piecewise Affine System

Purpose




The purpose of this paper is to probe the recursive identification of piecewise affine Hammerstein models directly by using input-output data. To explain the identification process of a parametric piecewise affine nonlinear function, the authors prove that the inverse function corresponding to the given piecewise affine nonlinear function is also an equivalent piecewise affine form. Based on this equivalent property, during the detailed identification process with respect to piecewise affine function and linear dynamical system, three recursive least squares methods are proposed to identify those unknown parameters under the probabilistic description or bounded property of noise.




Design/methodology/approach




First, the basic recursive least squares method is used to identify those unknown parameters under the probabilistic description of noise. Second, multi-innovation recursive least squares method is proposed to improve the efficiency lacked in basic recursive least squares method. Third, to relax the strict probabilistic description on noise, the authors provide a projection algorithm with a dead zone in the presence of bounded noise and analyze its two properties.




Findings




Based on complex mathematical derivation, the inverse function of a given piecewise affine nonlinear function is also an equivalent piecewise affine form. As the least squares method is suited under one condition that the considered noise may be a zero mean random signal, a projection algorithm with a dead zone in the presence of bounded noise can enhance the robustness in the parameter update equation.




Originality/value




To the best knowledge of the authors, this is the first attempt at identifying piecewise affine Hammerstein models, which combine a piecewise affine function and a linear dynamical system. In the presence of bounded noise, the modified recursive least squares methods are efficient in identifying two kinds of unknown parameters, so that the common set membership method can be replaced by the proposed methods.

Recursive least squares identification for piecewise affine Hammerstein models

Purpose




Emitter parameter estimation via signal sorting is crucial for communication, electronic reconnaissance and radar intelligence analysis. However, due to problems of transmitter circuit, environmental noises and certain unknown interference sources, the estimated emitter parameter measurements are still inaccurate and biased. As a result, it is indispensable to further refine the parameter values. Though the benchmark clustering algorithms are assumed to be capable of inferring the true parameter values by discovering cluster centers, the high computational and communication cost makes them difficult to adapt for distributed learning on massive measurement data. The paper aims to discuss these issues.




Design/methodology/approach




In this work, the author brings forward a distributed emitter parameter refinement method based on maximum likelihood. The author’s method is able to infer the underlying true parameter values from the huge measurement data efficiently in a distributed working mode.




Findings




Experimental results on a series of synthetic data indicate the effectiveness and efficiency of the author’s method when compared against the benchmark clustering methods.




Originality/value




With the refined parameter values, the complex stochastic parameter patterns could be discovered and the emitters could be identified by merging observations of consistent parameter values together. Actually, the author is in the process of applying her distributed parameter refinement method for PRI parameter pattern discovery and emitter identification. The superior performance ensures its wide application in both civil and military fields.

Xin Xu

Papers

Distributed emitter parameter refinement based on maximum likelihood