AN ALTERNATIVE PERSPECTIVE ON FUZZY SET-BASED APPROXIMATE REASONING

Abstract

On the subject of fuzzy set-based approximate reasoning, this paper attempts to present a perspective that is both rigorously stated and different from the standard approach as stated by Mamdani and many others. Functionally speaking, this alternative perspective produces results essentially the same as the standard one, and in fact we intend to prove that subject to certain assumptions the results will be exactly die same. We feel however that the alternative perspective offers advantages in that it makes a more appropriate and more understandable use of such concepts as fuzzy relations, the extension principle, and fuzzy composition. If indeed it can lead to a clearer and more widespread understanding of the approximate reasoning formalism then this could conceivably spark broader interest in fuzzy control and other applications.     

Multi-scale qualitative location: A direction-based model

Qualitative locations describe the locations of spatial objects by relating them to a reference frame with qualitative relations. Existing models concerned with regional partitions are mainly topology-based and do not consider the effects of scale changes on locations. This study develops a direction-based multi-scale qualitative location (DMQL) model to fill this gap. First, a cell partition is defined by extending the borders of the minimum bounding rectangles of the regions in a regional partition. Relating spatial objects to all regions by a set of directions is equal to representing the objects as a set of cells in a cell partition. Second, due to the multiple cell representations of spatial objects and the changes in direction relations across scales, some approaches are presented to derive the direction changes between regions in different frames, between spatial objects and regions, and between spatial objects at different scales. Third, the location and relation consistencies of qualitative locations are evaluated based on the cell representations of spatial objects at multiple scales through a case study. The results indicate that the DMQL model can locate objects more precisely than the topology-based models.     

Regression trees approach for flow-time prediction in wafer manufacturing processes using constraint-based genetic algorithm

Understanding the factors associated with the flow-time of wafer production is crucial for workflow design and analysis in wafer fabrication factories. Owing to wafer fabrication complexity, the traditional human approach to assigning the due-date is imprecise and prone to failure, especially when the shop status is dynamically changing. Therefore, assigning a due-date to each customer order becomes a challenge to production planning. The paper proposes a constraint-based genetic algorithm approach to determine the flow-time. The flow-time prediction model was constructed and compared with other approaches. Better computational effectiveness and prediction results from the constraint-based genetic algorithm are demonstrated using experimental data from a wafer-manufacturing factory.     

Symbolic Reasoning About Quantum Circuits in Coq

A quantum circuit is a computational unit that transforms an input quantum state to an output state.A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by it.However,when the number of qubits increases,the matrix dimension grows exponentially and the computation becomes intractable.In this paper,we propose a symbolic approach to reasoning about quantum circuits.It is based on a small set of laws involving some basic manipulations on vectors and matrices.This symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq,as demonstrated with some typical examples.     

Automated quantitative model-based fault diagnosistic protocol via Assumption State Differences

This treatment describes the details of a systematic protocol useful for performing optimal automated process fault analysis. This implementation generalizes the underlying Boolean logic version of the Method of Minimal Evidence (MOME) developed previously to a highly comprehensive algorithm for performing model-based fault diagnostics. This generalization allows for a more compact treatment of potential single and multiple fault situations, at all levels of possible diagnostic resolution, with both elegant and efficient uniform sensor validation and proactive fault analysis (SV&PFA) diagnostic rules for diagnosing those situations. This Assumption State Differences (ASD) Protocol version of the MOME algorithm thus automates the diagnostic reasoning necessary to continuously perform optimal process fault analysis so that only the underlying well-formulated models are required to achieve such performance. Using this algorithm consequently directly simplifies the solution of the more complicated problem of automated process fault analysis into the much more tractable, and incrementally solvable, problem of adequately modeling normal process operations.     

Specificity of meta-emotion effects on moral decision-making.

A recently proposed dual process theory of moral decision-making posits that utilitarian reasoning (approving of harmful actions that maximize good consequences) is the result of cognitive control of emotion. This suggests that deficits in emotional awareness will contribute to increased utilitarianism. The present study explored the relative contributions of the different facets of alexithymia and the closely related constructs of emotional intelligence and mood awareness to utilitarian decision making. Participants (N = 86) completed the Toronto Alexithymia Scale, Trait Meta Mood Scale, the Mood Awareness Scale, and a series of high-conflict, personal moral dilemmas validated by Greene et al. (2008). A brief neuropsychological battery was also administered to assess the possible confounds of verbal reasoning and abstract thinking ability. Principal components analysis revealed two latent factors—clarity of emotion and attention to emotion—which cut across all three meta-emotion instruments. Of these, low clarity of emotion—reflecting difficulty in reasoning thoughtfully about one's emotions—predicted utilitarian outcomes and provided unique variance beyond that of verbal and abstract reasoning abilities. Results are discussed in the context of individual differences in emotion regulation. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Expectations and interpretations during causal learning.

In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A–1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

船舶避碰智能决策支持系统研究

建立了一个基于RBR(Case-Based Reasoning,基于案例推理)和CBR(Rule-Based Reasoning,基于规则推理)结合的船舶避碰决策支持模型,将这个模型引入到船舶避碰智能决策支持系统IDSSVCA(Intelligent Decision Support System for Vessel...     

一个面向大批量定制的重用配置方法

为了提高产品配置的推理效率和降低企业内部变型产品的多样性,研究了面向大批量定制的重用配置技术.本文针对用户定制需求在一定时空范围内呈现出的相似性和集中性特点,从已有的产品配置结果入手,完成个性化配置求解任务.在基于广义产品结构的产品配置设计基础上,给出一种基于XML的配置结果描述方法;基于该描述方法,以用户定制页面为重...     

空间光学遥感器故障诊断技术

介绍了空间光学遥感器故障诊断系统结构和设计思想,列举了几种常见的空间光学遥感器故障诊断技术,比较了各自的优缺点,在此基础上提出了一种新的混合故障诊断技术,并对空间光学遥感器故障诊断技术进行了展望.