2学术论文的语言特点%1. 长短句的结合重要论点或技术性很强的信息 短句儿个长句+—个短句的节奏%1. 人称的使用I——>WeWe believe, We recommend, We propose%1. 主动一〉被动We prove that the results reflect the...Revised: It is proved that the results reflect the...避免使用youYou can see from the results in Figure 1,…Revised: As is shown in Figure 1,…%1. 选择更加正式的语言表达A. 正式的形容词a lot, many …>numerous, various, ...例： There are many different reasons.Revised: There are numerous reasons.对于方法的评价语言必然涉及到的形容词：少用good,bad,great, nice, poor,寻求更加书面的表达 描述研究的“好”和“坏”的正式形容词归纳：Positive Negativeinnovative flawed significant unsatisfactory rigorous thin impressive sloppy remarkable unwarranted elegant limitedB. 正式的副词Very―> highly, extremely, quite, rather...C. 正式的动词Get…> receive, obtain, achieve. ••The author reported/showed/proposed/developed/observed/demonstrated/presented The author contends/maintains/implied/claims The authorpoints out/notes/stresses/emphasizes/pays particular attention to D. 其他正式用语惯例(1) 避免使用缩写Dont,cant,isnt do not, can not, is not(2) 否定形式的占定句 肯定形式的占定句does not, do not, have not, can not...fail, miss, lack, neglect, deny, far from...(3) 避免不确定的语言etc.标题TitleInclude the topic of the studyInclude the scope of the studyMake it easy to understand without reading firstInclude key words to categorize and find in searchesFollow standard capitalization for your field (NOT ALL)IEEEhttp://ieeexplore.ieee.orgScience DirectAmerican Mathematical Societyhttp://www.ams.org反例:What is memory?Optimum Associative Neural Network Utilizing Maximum Likelihood摘要 Abstract全文的缩影Conference ProgramAbstract所应该包含的内容： developed method of •・・Concluding sentencesThe result of the experiments indicate that. •.Our work involving ... proving to be encouraging・In this paper, a new learning rule and theoretical analysis of an extended bidirectional associative memory network (MLBAM) is presented, by using the maximum likelihood criterion based on two well recognized and essential criteria, i.e., the convergence of the learning rule, and the noise tolerance of this network・ Traditional methods fail to distinguish highly approximative patterns. However, the method in our study can improve this by using the newly developed method of maximum likelihood criterion. In addition, the learning approach guarantees that correlated patterns could be associated as a stable state and the network possesses excellent anti-noise property by using likelihood function, namely, the learning approach specializes in the situation including stochastic disturbance. Additionally, the associative capability of the bidirectional associative memory is specifically discussed. Finally, two experiments are used to certify the validity and efficiency of our method, especially the methodes excellent anti-noise property by using likelihood function・ 介绍 IntroductionEstablish and Narrow the topic 建立/缩小主题Literature review研究评价(发展过程趋势)Investigation needed(Gap)指出空白Purpose of your research 研究目的Value of your research 研究价值Outline文章结构Establish the topicGeneral statement about the area of research being focused on establishing the narrowed research topic 研究大主题/背景… ＞论文研究小主题Establishing expression例:The solution of is a fundamental mathematical problem which arises in many fieldsof ・ The aim is to solve a set of simultaneously.The solution of a linear system of equations is a fundamental mathematical problem which arises in many fields of engineering and science. The aim is to solve a set of linear equations simultaneously.例:Recent advances in molecular biology such as cloning demonstrate that increasingly complex micromanipulation strategies for manipulating individual biology cells are required・ Microrobotic systems have the potential to change the way in which biological cells are studied and manipulated by enabling complex biomanipulation techniques. (International Journal of Robotics Research, pp. 64-75,2002 )Literature ReviewTrends in the researchHistorical development of this areaDifferent methodologies used研究发展趋势-〉不同方法的优缺点-〉最具特征的研究方法 have been noted to be conducive tools for many applications is one of the major topics in neural networks introduced which is powerful and has been universallynotedfl]-[21 has been extended to be by [3]-[4]. In , presented the theoryof by generalizing However, the defect of this traditional learning rule is obviousthat is limited and it fails to [8]-[17]. have been noted to be conducive tools for many applications is one of the major topics in neural networks introduced which is powerful and has been universallynotedfl]-[21 has been extended to be by In , presented the theoryof by generalizing However, the defect of this traditional learning rule is obviousthat is limited and it fails to [8卜[17]. have been noted to be conducive tools for many applications is one of the major topicsin neural networks introduced whic。