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

这项提案要求为一个正在进行的研究项目提供资金，该项目调查复杂模糊逻辑(CFL)的理论基础、可操作性和应用。十多年来，这一研究领域一直是我的研究小组的主要重点。在过去的五年里，我和我的学生们1)为CFL定义和表征了一组新的逻辑连接词，包括否定和反义词；2)开发并表征了基于CFL的机器学习体系结构(ANCFIS)，用于时间序列预测；以及3)改进了ANCFIS设计，以便在数据流挖掘中更快地进行培训和应用。在这些成功的基础上，我们现在询问，如果有任何CFL实例及其可操作性，将为大规模学习带来更好的性能、更紧凑和可解释的模型；以及与现有模型相比，这些新模型最有效地解决了哪些类别的问题。

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.