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@@ -464,10 +464,15 @@ <h2 id="方便的"><a href="#方便的" class="headerlink" title="方便的"></a
 <h2 id="简单的"><a href="#简单的" class="headerlink" title="简单的"></a>简单的</h2><p>it is straightforward to</p>
 <h2 id="最先进的"><a href="#最先进的" class="headerlink" title="最先进的"></a>最先进的</h2><p>State-of-the-art</p>
 <p>were widely considered to represent the state of the art in 。。。</p>
-<h1 id="英文论文写作"><a href="#英文论文写作" class="headerlink" title="英文论文写作"></a>英文论文写作</h1><h2 id="Introduction-介绍他人研究"><a href="#Introduction-介绍他人研究" class="headerlink" title="Introduction 介绍他人研究"></a>Introduction 介绍他人研究</h2><p><strong>conduct</strong>：Recently, shortterm deterministic sea-wave predictions (DSWPs) based on X-band radar measurements have been successfully conducted using phaserevolved wave components reconstructed from radar retrievals.</p>
+<h1 id="英文论文写作"><a href="#英文论文写作" class="headerlink" title="英文论文写作"></a>英文论文写作</h1><h2 id="Introduction"><a href="#Introduction" class="headerlink" title="Introduction"></a>Introduction</h2><h3 id="Introduction-介绍他人研究"><a href="#Introduction-介绍他人研究" class="headerlink" title="Introduction 介绍他人研究"></a>Introduction 介绍他人研究</h3><p><strong>conduct</strong>：Recently, shortterm deterministic sea-wave predictions (DSWPs) based on X-band radar measurements have been successfully conducted using phaserevolved wave components reconstructed from radar retrievals.</p>
 <p>**show:**Ferrandis et al.18 showed the potential of the data-driven model based on the LSTM model to simulate the nonlinear dynamics of a vessel in real-time.</p>
 <p><strong>propose</strong>:By extension, the encoder–decoder system19,20 was proposed for forecasting ship maneuvering in waves. The records of the various physical variables such as wave elevation, 6 degrees-of-freedom (DOF) motion responses, and rudder angle were input to the encoder, and the future time series of those variables were predicted through the decoder.</p>
 <p>**investigate:**Liu et al.22 investigated the prediction performance of the LSTM model according to the length of the adopted motion history (input vector space) in relation to the physical memory effects estimated by the impulse response function or auto-correlation function.</p>
+<h3 id="Introduction对文章结构介绍"><a href="#Introduction对文章结构介绍" class="headerlink" title="Introduction对文章结构介绍"></a>Introduction对文章结构介绍</h3><p><strong>present;introduce;give results and describe;presents the conclusions</strong>:The remainder of this article is organized as follows. Section II presents previous research in this domain. Section III introduces the predictors and their architecture&#x2F;parameters. Section IV gives results and describes the vessel and data selected for training and testing of the predictors. Section V presents the conclusions.</p>
+<p>Section 2 <strong>introduces</strong> the architecture and theoretical formula of the MSA-LSTM model. The optimization strategy and the two-stage training mechanism is <strong>pre sented</strong> in section 3. In Section 4, <strong>the results of</strong> ship motion prediction experiment and the analysis of prediction results are <strong>presented.</strong> Section 5 summarizes the results of this research.</p>
+<p>The remainder of this article is as follows: The ADWT algorithm and the structure of the hybrid model are <strong>presented</strong> in Section 2. The prediction performance of the hybrid prediction model for ship motion attitude in different ocean conditions is <strong>discussed</strong> in Section 3. Finally, the conclusion of the experiments and the summary of the entire study are represented in Section 4.</p>
+<p>The rest of this paper is structured as follows. In Section 2, a variable-order model of ship maneuvering motion is <strong>proposed</strong> in detail. Section 3 <strong>introduces</strong> the basic algorithms used in the whole program, including the basic principles of EKF, LS-SVM, and BP. Section 4 <strong>describes</strong> the framework of the MDIP model. Section 5 <strong>examines</strong> the simulation data for a container ship and compares in detail the two results <strong>derived</strong> from the MDIP scheme with the optimal-order mathematical model prediction result. Finally, c<strong>onclusions and future research directions are given</strong> in Section 6.</p>
+<p>This article is organized as follows. Section II <strong>introduces</strong> graph theory, GVFs, and radial basis function NNs (RBFNNs). Section III <strong>formulates</strong> the containment control problem under switching topologies. Section IV <strong>presents</strong> the proposed cooperative control architecture. Section V <strong>provides</strong> the stability analysis of the closed-loop control system. Section VI <strong>presents</strong> the simulation results. Section VII <strong>concludes</strong> this article.</p>
 <p><strong>在本文</strong></p>
 <p><strong>In the present work****，</strong>：In the present work, an input vector space optimization method is proposed based on the dependence hidden in ship motion records of a sequence.</p>
 <h2 id="本文提及另一篇文章的方法"><a href="#本文提及另一篇文章的方法" class="headerlink" title="本文提及另一篇文章的方法"></a>本文提及另一篇文章的方法</h2><p>More details about the length optimisation and difference method can be found in (Wang and Zou, 2018). </p>