## 1 Introduction
Despite the CEM method being effective at target identification, it is still susceptible to ambient noise, including unavoidable noises generated from working devices. Consequently, identifying targets using CEM is still restricted by the working environment. To address this shortcoming, this paper proposes utilizing a model that possesses noise immunity to enhance the effectiveness of the CEM method for Arctic sea ice identification. The CEM method can be transformed into a linear constrained optimization problem. Neural dynamics (ND) shows satisfactory performance in solving optimization problems, such as fast computational speed and good convergence, and has been studied by many scholars [38; 39; 40; 41]. For example, it can be applied to the motion generation and control of redundant manipulators [42], the tracking control of chaotic systems [43], and even to biological population control [44]. In practical applications, continuous models need to be discretized before being applied to hardware devices. This is different from most of the existing ND models that apply Euler difference formulas to discrete continuous models [45; 46; 47]. This paper applies a Taylor numerical difference formula [48] for discretization, which enables its steady-state error to reach \(O(\sigma^{3})\) smaller than that of Euler difference formulas with \(\sigma\) being the sampling period. Additionally, the absence of noise is always a prerequisite for the implementation of some models, but the presence of noise is unavoidable in practical working situations. To this end, a neural dynamics model with an integration term using the Taylor numerical difference formula for discretization, named error-accumulation enhanced neural dynamics (EAEND) model, is proposed to improve the stability and accuracy of the CEM method for Arctic sea ice identification. Thereafter, relevant convergence analysis and stability proofs are provided, which theoretically illustrate the convergence and noise immunity of the CEM method with the proposed EAEND model in noisy environments. Moreover, to assess the effectiveness of the CEM method integrated with the EAEND model for identifying Arctic sea ice, a series of comparative experiments are conducted on various sea ice observation images abiding by different noise environments. The outcomes highlight the superior noise immunity and benefits of incorporating the EAEND model. Figure 1 illustrates the flowchart representing the CEM method's process for Arctic sea ice identification assisted by the EAEND model. Specifically, this paper transforms the CEM scheme into an optimization problem. Subsequently, the EAEND model proposed in this paper is used to solve the optimization problem. To minimize the error and obtain an optimal filter coefficient for Arctic sea ice identification, an error function is constructed and the ND formula with an integral term is introduced. The error is then minimized  
using error-accumulative enhanced neural dynamics. Thus, the Arctic sea ice can be successfully identified. In the subsequent sections of this paper, the organization is as follows. Section 2 provides the model construction of the EAEND model; Section 3 gives the model simplification as well as theoretical proofs for the convergence and robustness of the EAEND model; Sections 4 and 5 present comparative identification experiments of different Arctic sea ice remote sensing images, while the proposed EAEND model is compared with other models; in Section 6, the experimental results are discussed and analyzed. Section 7 summarizes the whole paper. The contribution points of this paper are provided below. * The proposed EAEND model, using a Taylor numerical difference formula to enable steady-state error to reach \(O(\sigma^{3})\), is capable of improving the Arctic sea ice identification accuracy of the CEM method.