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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711618
• 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. Our proposal will support two Ph.D. students and five USRA students who will investigate CFL-based negation and antonym. We will explore the propositional and predicate CFLs that capture both, determining whether they are sound zzy systems and their applications to recommender systems and Internet advertising. We model antonym as a sign reversal of, and negation as orthogonal to, a concept; these are supported by a body of evidence in cognitive psychology and functional MRI studies. Deep networks, however, do not incorporate these mechanisms. There is also virtually no work on deep neural-fuzzy hybrids, nor on explanation mechanisms for deep networks. We are not aware of any work on antonym connectives in classical logics; in fuzzy logic, they are algorithms, not connectives. The proposed research thus explores ground-breaking directions in mathematical logic and deep learning. We will investigate high-value applications of the above developments, guided by our industrial partners. Our CRD grant with HybridForge, Inc. (along with J. Miller, U of A ECE) is focused on recommender engines and click-through-rate predictors. We are preparing funding applications with two more Alberta SMEs: XSENSOR Technologies and Addos Systems (the latter with J. Salmon, U of A ECE) on deep fuzzy systems in inferential sensing and condition monitoring (an M.Sc. and a PDF to be supported, respectively). All of these are commercially valuable products, through which 4 more HQP will be trained - 11 total.

本提案要求资助一项正在进行的研究计划，调查复杂模糊逻辑（CFL）的理论基础，运作和应用。十多年来，这个研究领域一直是我的研究小组的主要焦点。在过去的五年中，我和我的学生们已经定义并描述了一套新的CFL逻辑连接词，包括否定和反义词；2)开发并表征了一种基于cfl的时间序列预测机器学习架构（ANCFIS）；3)改进了ANCFIS设计，以实现更快的训练和数据流挖掘中的应用。在这些成功的基础上，我们现在询问是否有CFL实例，以及它们的操作，可以为大规模学习带来更好的性能，更紧凑和可解释的模型；这些新模型与现有模型相比，哪一类问题能最有效地解决？