Investigating the theory, operationalization, and practical applications of complex fuzzy logic

研究复杂模糊逻辑的理论、操作化和实际应用

• 批准号: RGPIN-2017-05335
• 负责人: Dick, Scott
• 金额: $ 1.68万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635036
• 关键词: Investigating; theory; operationalization; practical; applications

This proposal requests funding for an ongoing program of research investigating the theoretical foundations, operationalization, and application of Complex Fuzzy Logic (CFL). This area of study has been a primary focus of my research group for over ten years. In the past five years, my students and I have 1) defined and characterized a new set of logical connectives for CFL, incorporating both negation and antonym; 2) developed and characterized a CFL-based machine-learning architecture (ANCFIS) for time series forecasting; and 3) refined the ANCFIS design for faster training and applications in data stream mining. Building on these successes, we now inquire which if any CFL instances, and their operationalizations, lead to better-performing, and more compact and interpretable models for large-scale learning; and what classes of problems are most effectively solved by these new models versus existing ones.

该提案请求资助一项正在进行的研究计划，该研究计划调查复杂模糊逻辑（CFL）的理论基础、操作化和应用。十多年来，这一研究领域一直是我的研究小组的主要关注点。在过去的五年里，我和我的学生 1) 为 CFL 定义并描述了一套新的逻辑连接词，其中包含否定和反义词； 2) 开发并表征了用于时间序列预测的基于 CFL 的机器学习架构 (ANCFIS)； 3) 改进了 ANCFIS 设计，以实现数据流挖掘中更快的训练和应用。在这些成功的基础上，我们现在询问哪些 CFL 实例及其操作可以为大规模学习带来性能更好、更紧凑和可解释的模型；与现有模型相比，这些新模型能最有效地解决哪些类型的问题。