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3. To form a database on the past design activities which can help future design attempts. It should be easy to retrieve data and information by using a query facility on the database.
The database is formed and updated after each design session. At the end of every session, the system parameters, design specifications, control structure selected, control parameters obtained, design analysis results as well as written comments from the designer is stored as a relational database, together with basic information of the design session such as identity number, date, time and name of the designer. This is a very useful facility for the automated documentation of the design session. As the current version of CSDA is concerned with linear control system design only, the problem of traceability between derivative models does not appear. However, if CSDA is expanded in the future, then information on relationships between different models has to be stored by introducing reference pointers in each database item.
3. Analysis/Design Block
This block is the core of the CSDA system. Details of the control structure selection module and the control parameters calculation module are explained here. A pre-design analysis module and a post-design analysis module are mentioned briefly. A command signal generation module and a parameter optimizer module will be included in the future. The authors have examined several control algorithms to select a satisfactory control structure and its parameters. These algorithms are written as user-defined functions in MATLAB. One special feature of the CSDA system is an “Auto design” button. This button aims to give an automated design solution to the user. This includes a selection of the control structure and a suitable design of the controller parameters. The previous results in designing a controller are also saved. Hence, the system can also choose a satisfactory control structure and decide on its parameters with reference to previous design results.
新的适应CBR方法结合关系分析机械设计问题解决方案.论文网
基于案例的推理(CBR)方法被证明是一种很有前途的方法在确定新的机械产品的参数值调整以前成功的解决当前的问题。较复杂的情况下检索技术,适应下最后在CBR研究仍然是一个瓶颈问题,迫切需要解决。根据参数机械设计(PMD)的特点,即。,更少的数据,许多参数,本文采用加权平均数(WM)作为基本模型,并提出了一种新的适应CBR方法PMD的结合问题解决方案(PS)关系的信息。之前在我们提出的适应方法,适应类似的情况下,灰色关联分析(草)是利用调查PS关系信息隐藏在K检索情况下,该方法称为GRA-WM。不同于古典WM方法,检索案例为每个解决方案元素的权重因子适应计算相似度矩阵(SM)和关系矩阵乘以(RM)和新的机械产品的调整解决方案价值随后通过计算解决方案的加权平均的K值相似的情况下。电力变压器设计的案例研究证明GRA-WM的工业适用性。此外,实证比较GRA-WM和其他适应方法进行验证其优越性。实证结果表明,GRA-WM可以提供一个可接受的适应提议在CBR应用机械设计.