微波滤波器智能设计方法的研究
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摘要
基于微波滤波器设计技术的现状,针对平面无源微波滤波器的特点,将现代设计理论引入产品的设计过程,对产品智能设计中涉及的知识表示、获取、推理以及设计方案的产生与综合评估等内容进行了研究,并提出了相应的模型和解决方法。这些方法的提出,对于构建产品智能设计系统,实现微波滤波器设计的智能化,提升制造业的设计水平,缩短产品研发周期和加速产品更新换代以及智能设计理论的研究都具有重要理论理论意义和实用价值。
提出了框架式数据结构的产品模型,使设计问题的求解过程转化为产品模型的匹配和识别过程。该模型混合使用了语义网络、框架、规则、过程等知识表示方法将多种异构知识联系在一起,并以不精确逻辑为基础,联合采用了基于案例推理、基于规则推理和基于模型的推理方法获得设计解。采用类比推理、模糊聚类分析和数理统计的方法从成功案例中提取领域知识,实现了机器的自学习功能。
提出了一种广义贴近度的算法,可合理地处理普通实数变量、实数域上的区间值以及模糊集合变量。该算法应用于基于模糊模式识别原理和模糊案例推理的概念设计过程中,有效地解决了设计中的模糊不确定性问题。提出了加权证据的概念和综合评估模型,可同时处理评估资源中的多种不确
定因素。利用模型的输出可给出评估结果的确定性和不确定性的测度,从而对评估结果作出合理解释,为设计方案的取舍、择优和修改提供决策依据。
The modern design theory was introduced in the design of the planar microwave filters. This thesis focuses on the representation, capturing, the reasoning of the knowledge, and the comprehensive evaluation to the design schemes in planar microwave filter intelligent designing and the corresponding solutions are proposed, which is of important of significance to realize the intelligent design for microwave filter products and to the intelligent design theory.
A product model with frame data structure was set up to change finding the design solution to pattern matching or recognition. Semantic network, frame, rule, procedure were combined to represent the knowledge in different structures, and case-based reasoning, rule-based reasoning and model-based reasoning were used to find the conceptual design solution at the basis of non-precise logic. Analogical inference, fuzzy clustering analyzing and statistics methods are used to capture the knowledge from those existing success cases, the self-learning was realized.
A generalized closeness degree algorithm was proposed suitable for three kinds of variables of general real number, the section on the real number domain and fuzzy number. It was used in conceptual design based on fuzzy pattern recognition and fuzzy case-based reasoning to deal with fuzzy information effectively.
A comprehensive evaluation model with weighted evidence was introduced to deal with various uncertainty factors. From the output of this model, the measurement of the certainty or uncertainty of the evaluation result can be obtained, which is the basic evidence to adopt or discard the design scheme, to find the optimal design or to revise the existing design.
提出了框架式数据结构的产品模型,使设计问题的求解过程转化为产品模型的匹配和识别过程。该模型混合使用了语义网络、框架、规则、过程等知识表示方法将多种异构知识联系在一起,并以不精确逻辑为基础,联合采用了基于案例推理、基于规则推理和基于模型的推理方法获得设计解。采用类比推理、模糊聚类分析和数理统计的方法从成功案例中提取领域知识,实现了机器的自学习功能。
提出了一种广义贴近度的算法,可合理地处理普通实数变量、实数域上的区间值以及模糊集合变量。该算法应用于基于模糊模式识别原理和模糊案例推理的概念设计过程中,有效地解决了设计中的模糊不确定性问题。提出了加权证据的概念和综合评估模型,可同时处理评估资源中的多种不确
定因素。利用模型的输出可给出评估结果的确定性和不确定性的测度,从而对评估结果作出合理解释,为设计方案的取舍、择优和修改提供决策依据。
The modern design theory was introduced in the design of the planar microwave filters. This thesis focuses on the representation, capturing, the reasoning of the knowledge, and the comprehensive evaluation to the design schemes in planar microwave filter intelligent designing and the corresponding solutions are proposed, which is of important of significance to realize the intelligent design for microwave filter products and to the intelligent design theory.
A product model with frame data structure was set up to change finding the design solution to pattern matching or recognition. Semantic network, frame, rule, procedure were combined to represent the knowledge in different structures, and case-based reasoning, rule-based reasoning and model-based reasoning were used to find the conceptual design solution at the basis of non-precise logic. Analogical inference, fuzzy clustering analyzing and statistics methods are used to capture the knowledge from those existing success cases, the self-learning was realized.
A generalized closeness degree algorithm was proposed suitable for three kinds of variables of general real number, the section on the real number domain and fuzzy number. It was used in conceptual design based on fuzzy pattern recognition and fuzzy case-based reasoning to deal with fuzzy information effectively.
A comprehensive evaluation model with weighted evidence was introduced to deal with various uncertainty factors. From the output of this model, the measurement of the certainty or uncertainty of the evaluation result can be obtained, which is the basic evidence to adopt or discard the design scheme, to find the optimal design or to revise the existing design.
引文
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