改进的模糊推理规则图像边缘检测算法

图像边缘检测是数字图像处理领域的关键技术,边缘检测的结果决定了图像后续处理的质量。模糊推理规则边缘检测算法具有较强的边缘检测能力,并且具备一定的抗噪效果。但是,这种算法只在高斯噪声较小时有效,当高斯噪声较大时它的边缘检测效果甚至比Canny等算子的效果还差。针对模糊推理规则算法在强高斯噪声时效果较差的问题,提出一种改进的模糊边缘检测算法。该算法能够根据图像含噪情况调整边缘检测方案：当噪声较弱时,使用模糊推理规则边缘检测算法;当噪声较强时,为提高算法抑制噪声的能力,使用改进的模糊推理规则边缘检测算法。实验结果表明,该方法具有更好的抗噪性能和边缘检测能力。     

Adaptive fuzzy inference neural network

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.     

基于改进型模糊聚类的模糊系统建模方法

结合减法聚类和模糊C均值聚类,提出了一种改进型聚类算法,加快了收敛速度．利用改进后的算法对模糊系统输入或输出的样本集聚类,对聚类结果采用Trust-Region法拟合高斯型和S型函数,以实现模糊系统输入、输出空间的划分和隶属度函数参数的确定．结合MATLAB的模糊和曲线拟合工具箱,详述了如何在标准算法上进行改进和模糊系统建模．通过对IRIS标准数据聚类实验以及在解决机械加工误差复映问题上的应用,验证了改进后算法和建模方法的有效性．     

基于模糊推理的边缘检测新算法

研究了原有的基于模糊推理的边缘检测算法。在分析原有算法存在问题的基础上,提出了一种新的模糊化规则,利用方向灰度对比度去确定边缘隶属度值,增加了去除伪边缘的规则,使得边缘细化。对原有算法和新算法进行了品质因素和平均运行时间的算法性能的对比、分析。     

基于二叉树型分层的广义混合模糊系统推理规则数的缩减

为避免广义混合模糊系统因输入变量个数的增加而引起规则爆炸现象,应用二叉树型分层方法给出混合推理规则,进而对广义混合模糊系统的输入实施二叉树型分层,从理论上获得了该系统分层后的输入输出表达式和推理规则总数的计算公式.此外,通过实例对该系统分层和不分层的规则总数进行了比较和分析,结果表明分层后广义混合模糊系统可大幅度缩减推理规则总数,并可有效地避免规则爆炸.     

A computational method of forecasting based on fuzzy time series

In this paper, a computational method of forecasting based on fuzzy time series have been developed to provide improved forecasting results to cope up the situation containing higher uncertainty due to large fluctuations in consecutive year's values in the time series data and having no visualization of trend or periodicity. The proposed model is of order three and uses a time variant difference parameter on current state to forecast the next state. The developed model has been tested on the historical student enrollments, University of Alabama to have comparison with the existing methods and has been implemented for forecasting of a crop production system of lahi crop, containing higher uncertainty. The suitability of the developed model has been examined in comparison with the other models to show its superiority.     

The construction of a fuzzy inference network by extension of the rule inference network

Fuzzy logic can bring about inappropriate inferences as a result of ignoring some information in the reasoning process. Neural networks are powerful tools for pattern processing, but are not appropriate for the logical reasoning needed to model human knowledge. The use of a neural logic network derived from a modified neural network, however, makes logical reasoning possible. In this paper, we construct a fuzzy inference network by extending the rule–inference network based on an existing neural logic network. The propagation rule used in the existing rule–inference network is modified and applied. In order to determine the belief value of a proposition pertaining to the execution part of the fuzzy rules in a fuzzy inference network, the nodes connected to the proposition to be inferenced should be searched for. The search costs are compared and evaluated through application of sequential and priority searches for all the connected nodes.     

基于自适应直觉模糊推理的数据挖掘方法

针对数据挖掘问题,将直觉模糊集与神经网络理论相结合,提出一种新的方法。用自适应直觉模糊推理的方法来解决数据挖掘问题,该方法可以根据直觉模糊神经网络本身的自适应学习能力来调节网络参数,自动生成规则库。最后通过一个仿真实例证明了该方法的有效性。     

基于小波隶属函数的模糊推理规则优化

隶属函数决定着模糊集的特征,建立小波基函数与隶属函数之间的联系,从而利用小波分析探讨模糊推理的实质,以一种非对称Haar小波基与三角型、梯型隶属函数的对应关系为基础,将小波分析、遗传算法与模糊系统结合,利用遗传算法实现小波隶属函数的训练学习,进而实现模糊推理规则的优化。     

基于小波隶属函数的模糊推理规则优化

隶属函数决定着模糊集的特征，建立小波基函数与隶属函数之间的联系，从而利用小波分析探讨模糊推理的实质，以一种非对称Haar小波基与三角型、梯型隶属函数的对应关系为基础，将小波分析、遗传算法与模糊系统结合，利用遗传算法实现小波隶属函数的训练学习，进而实现模糊推理规则的优化。     

基于最大熵模型和规则的中文姓名识别

中文姓名识别是中文信息处理的一项重要技术,识别的召回率对其它需要以姓名识别为基础的中文信息处理技术有至关重要的影响。提出了一种统计模型和处理规则相结合的中文姓名识别方法：首先以最大熵模型识别潜在姓氏,而后再通过判定规则作进一步处理。真实语料的开放测试表明,该方法在召回率方面有明显的优势,可以达到94%以上的召回率,同时能保证较高的准确率。     

A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches. Cane Wing-ki Leung is a PhD student in the Department of Computing, The Hong Kong Polytechnic University, where she received her BA degree in Computing in 2003. Her research interests include collaborative filtering, data mining and computer-supported collaborative work. Stephen Chi-fai Chan is an Associate Professor and Associate Head of the Department of Computing, The Hong Kong Polytechnic University. Dr. Chan received his PhD from the University of Rochester, USA, worked on computer-aided design at Neo-Visuals, Inc. in Toronto, Canada, and researched in computer-integrated manufacturing at the National Research Council of Canada before joining the Hong Kong Polytechnic University in 1993. He is currently working on the development of collaborative Web-based information systems, with applications in education, electronic commerce, and manufacturing. Fu-lai Chung received his BSc degree from the University of Manitoba, Canada, in 1987, and his MPhil and PhD degrees from the Chinese University of Hong Kong in 1991 and 1995, respectively. He joined the Department of Computing, Hong Kong Polytechnic University in 1994, where he is currently an Associate Professor. He has published widely in the areas of computational intelligence, pattern recognition and recently data mining and multimedia in international journals and conferences and his current research interests include time series data mining, Web data mining, bioinformatics data mining, multimedia content analysis,and new computational intelligence techniques.     

Memory-aware and context-aware multi-DNN inference on the edge

Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices, especially for real time image-based analysis. Increasingly, multi-faced knowledge is extracted by executing multiple DNNs inference models, e.g., identifying objects, faces, and genders from images. It is of paramount importance to guarantee low response times of such multi-DNN executions as it affects not only users quality of experience but also safety. The challenge, largely unaddressed by the state of the art, is how to overcome the memory limitation of edge devices without altering the DNN models. In this paper, we design and implement Masa, a responsive memory-aware multi-DNN execution and scheduling framework, which requires no modification of DNN models. The aim of Masa is to consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. The enabling features of Masa are (i) modeling inter- and intra-network dependency, (ii) leveraging complimentary memory usage of each layer, and (iii) exploring the context dependency of DNNs. We verify the correctness and scheduling optimality via mixed integer programming. We extensively evaluate two versions of Masa, context-oblivious and context-aware, on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different generation patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 90% on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.     

Fuzzy modifiers based on fuzzy relations

In this paper we introduce a new type of fuzzy modifiers (i.e. mappings that transform a fuzzy set into a modified fuzzy set) based on fuzzy relations. We show how they can be applied for the representation of weakening adverbs (more or less, roughly) and intensifying adverbs (very, extremely) in the inclusive and the non-inclusive interpretation. We illustrate their use in an approximate reasoning scheme.     

A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules

Fuzzy set theory has been used as an approach to deal with uncertainty in the supplier selection decision process. However, most studies limit applications of fuzzy set theory to outranking potential suppliers, not including a qualification stage in the decision process, in which non-compensatory types of decision rules can be used to reduce the set of potential suppliers. This paper presents a supplier selection decision method based on fuzzy inference that integrates both types of approaches: a non-compensatory rule for sorting in qualification stages and a compensatory rule for ranking in the final selection. Fuzzy inference rules model human reasoning and are embedded in the system, which is an advantage when compared to approaches that combine fuzzy set theory with multicriteria decision making methods. Fuzzy inference combined with a fuzzy rule-based classification method is used to categorize suppliers in qualification stages. Classes of supplier performance can be represented by linguistic terms, which allow decision makers to deal with subjectivity and to express qualification requirements in linguistic formats. Implementation of the proposed method and techniques were analyzed and discussed using an illustrative case. Three defuzzification operators were used in the final selection, yielding the same ranking. Factorial design was applied to test consistency and sensitivity of the inference rules. The findings reinforce the argument that including stages of qualification based on fuzzy inference and categorization makes this method especially useful for selecting from a large set of potential suppliers and also for first time purchase.     

On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets

When performing a classification task, we may find some data-sets with a different class distribution among their patterns. This problem is known as classification with imbalanced data-sets and it appears in many real application areas. For this reason, it has recently become a relevant topic in the area of Machine Learning.The aim of this work is to improve the behaviour of fuzzy rule based classification systems (FRBCSs) in the framework of imbalanced data-sets by means of a tuning step. Specifically, we adapt the 2-tuples based genetic tuning approach to classification problems showing the good synergy between this method and some FRBCSs.Our empirical results show that the 2-tuples based genetic tuning increases the performance of FRBCSs in all types of imbalanced data. Furthermore, when the initial Rule Base, built by a fuzzy rule learning methodology, obtains a good behaviour in terms of accuracy, we achieve a higher improvement in performance for the whole model when applying the genetic 2-tuples post-processing step. This enhancement is also obtained in the case of cooperation with a preprocessing stage, proving the necessity of rebalancing the training set before the learning phase when dealing with imbalanced data.     

Designing of classifiers based on immune principles and fuzzy rules

This paper proposed an algorithm to design a fuzzy classification system based on immune principles. The proposed algorithm evolves a population of antibodies based on the clonal selection and hypermutation principles. The membership function parameters and the fuzzy rule set including the number of rules inside it are evolved at the same time. Each antibody (candidate solution) corresponds to a fuzzy classification rule set. We compared our algorithm with other classification schemes on some benchmark datasets. The results demonstrated the effectiveness of the proposed immune algorithm.     

Fuzzy forecasting based on fuzzy time series

This article presents an improved method of fuzzy time series to forecast university enrollments. The historical enrollment data of the University of Alabama were first adopted by Song and Chissom (Song, Q. and Chissom, B. S. (1993). Forecasting enrollment with fuzzy time series-part I, Fuzzy Sets and Systems, 54, 1–9; Song, Q. and Chissom, B. S. (1994). Forecasting enrollment with fuzzy time series-part II, Fuzzy Sets and Systems, 54, 267–277) to illustrate the forecasting process of the fuzzy time series. Later, Chen proposed a simpler method. In this article, we show that our method is as simple as Chen's method but more accurate. In forecasting the enrollment of the University of Alabama, the root mean square percentage error (RMSPE) of our method is 3.1113% while the RMSPE of Chen's method is 4.0516%, which shows that our method is doing much better.     

基于模糊推理图的故障诊断

根据系统元件之间的连接结构构建故障诊断模糊推理图,通过对模糊推理图进行化简及模糊运算实现对系统的故障诊断。以观测点作为属性准则形成诊断矩阵,在推理图中按照原因-结果对进行连续推理,获得一个模糊诊断的优先级别排列,以减少故障的排查时间。     

基于模糊逻辑和纹理分析的图像增强算法研究

提出了基于模糊逻辑和纹理分析的图像增强算法,通过图像模糊化、提取纹理信息和纹理信息模糊化、定义局部对比度、根据全局和局部信息来进行对比度的变换等措施,提高了增强算法的效果。测试结果表明该算法能很好地增强图像的边缘等细节信息,同时避免放大噪声和过增强的出现。