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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=649256
• 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 & complete. We will design deep fuzzy systems, which we expect will yield more interpretable models than existing deep networks. Deep complex fuzzy systems will be an extension that employs negation and antonym to also create more compact networks (just as negative rules are known to lead to more compact rulebases). We will apply these architectures to several high-value problems we have explored in previous research. Two other Ph.D. students, supported by a CRD grant, will examine shallow complex fuzzy 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.

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。 ******我们的提案将支持两名博士。学生和五名 USRA 学生将研究基于 CFL 的否定和反义词。我们将探索捕获两者的命题和谓词 CFL，确定它们是否健全且完整。我们将设计深度模糊系统，我们预计它将产生比现有深度网络更可解释的模型。深度复杂模糊系统将是一种扩展，它使用否定和反义词来创建更紧凑的网络（正如已知否定规则会导致更紧凑的规则库一样）。我们将把这些架构应用到我们在之前的研究中探索过的几个高价值问题。另外两名博士。学生们在 CRD 资助的支持下，将研究浅层复杂模糊系统及其在推荐系统和互联网广告中的应用。 ******我们将反义词建模为概念的符号反转，将否定建模为与概念正交；这些都得到了认知心理学和功能性核磁共振研究中大量证据的支持。然而，深层网络不包含这些机制。实际上还没有关于深度神经模糊混合的工作，也没有关于深度网络解释机制的工作。我们不知道古典逻辑中有任何关于反义连接词的工作；在模糊逻辑中，它们是算法，而不是连接词。因此，拟议的研究探索了数理逻辑和深度学习的突破性方向。******我们将在工业合作伙伴的指导下研究上述发展的高价值应用。我们与 HybridForge, Inc.（以及阿拉巴马大学 ECE 的 J. Miller 一起）提供的 CRD 资助主要集中在推荐引擎和点击率预测器上。我们正在准备与另外两家艾伯塔省中小企业合作的资助申请：XSENSOR Technologies 和 Addos Systems（后者与阿尔伯塔大学 ECE 的 J. Salmon 合作），用于推理传感和状态监测的深度模糊系统（分别支持硕士学位和 PDF）。所有这些都是具有商业价值的产品，将通过这些产品再培训 4 名总部人员，总共 11 名。