Supervised Pseudo Self-Evolving Cerebellar algorithm for generating fuzzy membership functions

The proper generation of fuzzy membership function is of fundamental importance in fuzzy applications. The effectiveness of the membership functions in pattern classifications can be objectively measured in terms of interpretability and classification accuracy in the conformity of the decision boundaries to the inherent probabilistic decision boundaries of the training data. This paper presents the Supervised Pseudo Self-Evolving Cerebellar (SPSEC) algorithm that is bio-inspired from the two-stage development process of the human nervous system whereby the basic architecture are first laid out without any activity-dependent processes and then refined in activity-dependent ways. SPSEC first constructs a cerebellar-like structure in which neurons with high trophic factors evolves to form membership functions that relate intimately to the probability distributions of the data and concomitantly reconcile with defined semantic properties of linguistic variables. The experimental result of using SPSEC to generate fuzzy membership functions is reported and compared with a selection of algorithms using a publicly available UCI Sonar dataset to illustrate its effectiveness.     

Incorporating fuzzy membership functions into the perceptron algorithm

The perceptron algorithm, one of the class of gradient descent techniques, has been widely used in pattern recognition to determine linear decision boundaries. While this algorithm is guaranteed to converge to a separating hyperplane if the data are linearly separable, it exhibits erratic behavior if the data are not linearly separable. Fuzzy set theory is introduced into the perceptron algorithm to produce a ``fuzzy algorithm' which ameliorates the convergence problem in the nonseparable case. It is shown that the fuzzy perceptron, like its crisp counterpart, converges in the separable case. A method of generating membership functions is developed, and experimental results comparing the crisp to the fuzzy perceptron are presented.     

基于改进多层次模糊关联规则的定量数据挖掘算法

针对单一层次结构实现规则提取具有规则提取准确性不高、算法运行时间长、难以满足用户使用需求的问题,提出一种基于改进多层次模糊关联规则的定量数据挖掘算法。采用高频项目集合,通过不断深化迭代的方法形成自顶向下的挖掘过程,整合模糊集合理论、数据挖掘算法以及多层次分类技术,从事务数据集中寻找模糊关联规则,挖掘出储存在多层次结构事务数据库中定量值信息的隐含知识,实现用户的定制化信息挖掘需求。实验结果表明,提出的数据挖掘算法在挖掘精度和运算时间方面相较于其他算法具有突出优势,可为多层次关联规则提取方法的实际应用带来新的发展空间。     

Solving the mesh-partitioning problem with an ant-colony algorithm

Many real-world engineering problems can be expressed in terms of partial differential equations and solved by using the finite-element method, which is usually parallelised, i.e. the mesh is divided among several processors. To achieve high parallel efficiency it is important that the mesh is partitioned in such a way that workloads are well balanced and interprocessor communication is minimised. In this paper we present an enhancement of a technique that uses a nature-inspired metaheuristic approach to achieve higher-quality partitions. The so-called multilevel ant-colony algorithm, which is a relatively new metaheuristic search technique for solving optimisation problems, was applied and studied, and the possible parallelisation of this algorithm is discussed. The multilevel ant-colony algorithm performed very well and is superior to classical k-METIS and Chaco algorithms; it is even comparable with the combined evolutionary/multilevel scheme used in the JOSTLE evolutionary algorithm and returned solutions that are better than the currently available solutions in the Graph Partitioning Archive.     

A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding

The multi-level thresholding is a popular method for image segmentation. However, the method is computationally expensive and suffers from premature convergence when level increases. To solve the two problems, this paper presents an advanced version of gravitational search algorithm (GSA), namely hybrid algorithm of GSA with genetic algorithm (GA) (GSA-GA) for multi-level thresholding. In GSA-GA, when premature convergence occurred, the roulette selection and discrete mutation operators of GA are introduced to diversify the population and escape from premature convergence. The introduction of these operators therefore promotes GSA-GA to perform faster and more accurate multi-level image thresholding. In this paper, two common criteria (1) entropy and (2) between-class variance were utilized as fitness functions. Experiments have been performed on six test images using various numbers of thresholds. The experimental results were compared with standard GSA and three state-of-art GSA variants. Comparison results showed that the GSA-GA produced superior or comparative segmentation accuracy in both entropy and between-class variance criteria. Moreover, the statistical significance test demonstrated that GSA-GA significantly reduce the computational complexity for all of the tested images.     

Image segmentation by a fuzzy clustering algorithm using adaptivespatially constrained membership functions

We present an adaptive fuzzy clustering scheme for image segmentation, the adaptive fuzzy clustering/segmentation (AFCS) algorithm. In AFCS, the nonstationary nature of images is taken into account by modifying the prototype vectors as functions of the sample location in the image. The inherent high interpixel correlation is modeled using neighborhood information. A multiresolution model is utilized for estimating the spatially varying prototype vectors for different window sizes. The fuzzy segmentations at different resolutions are combined using a data fusion process in order to compute the final fuzzy partition matrix. The results provide segmentations, having lower fuzzy entropy when compared to the possibilistic C-means algorithm, while maintaining the image's main characteristics. In addition, due to the neighborhood model, the effects of noise in the form of single pixel regions are minimized     

A hybrid GA-SA algorithm for just-in-time scheduling of multi-level assemblies

The environment is a manufacturing facility that produces multi-level assemblies in a Just-In-Time (JIT) fashion. The due-dates and lot-sizes of the end-items are given, and the objective is to determine a lot-for-lot operations schedule that minimizes the cumulative production lead-time. The scheduling problem within such an environment is NP-hard, and therefore, the performance of heuristics may vary depending on the specific problem instance. To address this problem an effective hybrid Genetic Algorithm-Simulated Annealing (GA-SA) algorithm is developed. The GA starts with an initial population generated by well known scheduling heuristics, a critical path heuristic, and randomly generated schedules. The scheduling work is shared by the GA and SA in two phases that alternate until convergence: (1) Phase I is the GA that crosses over solutions for different work-centers, and (2) Phase II is the SA that improves the sequence of operations on individual work-centers. The effectiveness of the proposed heuristic is assessed via numerical studies.     

An improved approach to find membership functions and multiple minimum supports in fuzzy data mining

Fuzzy mining approaches have recently been discussed for deriving fuzzy knowledge. Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions. In that paper, minimum supports and membership functions of all items are encoded in a chromosome such that it may be not easy to converge. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is called divide-and-conquer genetic-fuzzy mining algorithm for items with Multiple Minimum Supports (DGFMMS), and is designed for finding minimum supports, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item’s minimum support and membership functions. The final best minimum supports and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach.     

Optimization of multiple input–output fuzzy membership functions using clonal selection algorithm

A clonal selection algorithm (CLONALG) inspires from clonal selection principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the study is to determine the membership functions. Proposed method has been implemented using a developed CLONALG program for a multiple input–output (MI–O) fuzzy system. In this study, GA and binary particle swarm optimization (BPSO) are used for implementing the proposed method as well and they are compared. It has been shown that using clonal selection algorithm is advantageous for finding optimum values of fuzzy membership functions     

On elicitation of membership functions

In this study, we elaborate on an important issue of membership function determination. The main point is that any membership estimation procedure should reconcile the semantics of a fuzzy set (regarded as an information granule arising at some level of information abstraction) with the experimental evidence conveyed by numeric data. This, in the sequel, calls for the development of the hybrid two-phase approach that starts from a rough specification of the support of the fuzzy set that is followed by detailed computations involving a specific type of membership function and an estimation of its parameters. The role of robust statistics in this setting is also raised. A number of experimental results are discussed.     

A fast algorithm for mining association rules

In this paper,the problem of discovering association rules between items in a large database of sales transactions is discussed.and a novel algorithm,BitMatrix,is proposed.The proposed algorithm is fundamentally different from the known algorithms Apriori and AprioriTid.Empirical evaluation shows that the algorithm outperforms the known ones for large databases.Scale-up experiments show that the algorithm scales linearly with the number of transactions.     

New multi-level algorithm for linear dynamical systems

A new approach is developed for the solution of linear interconnected dynamical systems. The algorithm is within the class of non-feasible methods. Its main feature is that it allows the transfer of information between the subsystems with each iteration. The convergence properties of this technique are given in detail. It is shown that the proposed methodology may be faster than the well-known prediction algorithm given in the literature since in each iteration better interconnection information is used within the period of the optimization horizon.     

一种快速的频繁子图挖掘算法

提出了一种基于关联矩阵的频繁子图挖掘算法。该算法通过对关联矩阵的标准化,有效地降低了子图同构判断的代价。在此基础上,算法利用深度优先的思想,通过逐步扩展频繁边找出所有频繁子图。实验结果表明,该算法比其他同类算法具有更快的速度和更好的稳定性。     

Maximum entropy membership functions for discrete fuzzy variables

Due to the deficiency of information, the membership function of a fuzzy variable cannot be obtained explicitly. It is a challenging work to find an appropriate membership function when certain partial information about a fuzzy variable is given, such as expected value or moments. This paper solves such problems for discrete fuzzy variables via maximum entropy principle and proves some maximum entropy theorems with certain constraints. A genetic algorithm is designed to solve the general maximum entropy model for discrete fuzzy variables, which is illustrated by some numerical experiments.     

A group membership algorithm with a practical specification

Presents a solvable specification and gives an algorithm for the group membership problem in asynchronous systems with crash failures. Our specification requires processes to maintain a consistent history in their sequences of views. This allows processes to order failures and recoveries in time and simplifies the programming of high level applications. Previous work has proven that the group membership problem cannot be solved in asynchronous systems with crash failures. We circumvent this impossibility result building a weaker, yet nontrivial specification. We show that our solution is an improvement upon previous attempts to solve this problem using a weaker specification. We also relate our solution to other methods and give a classification of progress properties that can be achieved under different models     

A new method for constructing membership functions and fuzzy rulesfrom training examples

To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the alpha-cuts of equivalence relations and the alpha-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.     

基于遗传-蚁群算法的无线Mesh网QoS路由算法研究

针对无线Mesh网QoS的路由特点,结合遗传算法扣蚁群算法的特性,设计了一种遗传算法和蚁群算法相融合的算法,提出了遗传-蚁群算法求解无线Mesh网QoS路由问题的解决方案.该算法采用遗传算法生成初始信息素分布,利用蚁群算法求精确解,并在遗传算法运行过程中动态确定遗传算法与蚁群算法的最佳融合时机,实现两个算法的优势互补.实验结果表明,该算法在无线Mesh网QoS路由选择中是高效的,性能明显优于遗传算法和蚁群算法.     

基于蚁群–混合蛙跳算法的贴片机贴装顺序优化

针对喂料器的位置确定的条件下,研究拱架式贴片机的元器件贴装顺序优化问题.建立了新的拱架式贴片机贴装顺序的数学模型.针对问题的路径寻优特点,把混合蛙跳算法与蚁群算法相融合,实现对贴片机的元件贴装顺序优化问题的求解.在算法中提出了适应于贴片机实际贴装情况的分段启发函数、分段信息素以及信息素的分段更新策略等多种改进方法.为验证算法有效性,以20块实际生产的PCB为实例进行了测试.实验结果表明,算法具有较好的求解精度和全局搜索能力,与文献中的单一混合蛙跳算法相比,平均效率提高了7.89%;与蚁群算法相比,平均效率提高了3.79%.     

Data mining based multi-level aggregate service planning for cloud manufacturing

Cloud manufacturing (CMfg) promotes a dynamic distributed manufacturing environment by connecting the service providers and manages them in a centralized way. Due to the distinct production capabilities, the service providers tend to be delegated services of different granularities. Meanwhile, users of different types may be after services of different granularities. A traditional aggregate production planning method is often incapable of dealing with type of problems. For this reason, a multi-level aggregate service planning (MASP) methodology is proposed. The MASP service hierarchy is presented, which integrates the services of different granularities into a layered structure. Based on this structure, one of data mining technologies named time series is introduced to provide dynamic forecast for each layer. In this way, MASP can not only deal with the services of multi-granularity, but also meet the requirements of all related service providers irrespective of their manufacturing capabilities. A case study has been carried out, showing how MASP can be applied in a CMfg environment. The results of the prediction are considered reliable as the order of magnitude of the production for each service layer is much greater than that of the corresponding mean forecast error.     

基于多层次聚类的文本知识挖掘

为解决在互联网文本信息爆炸性增长的前提下,在大规模文本数据中如何发现隐含的、有价值的潜在知识的问题,提出基于多层次文本聚类的文本知识挖掘方法,针对不同规模的文本数据进行不同粒度的聚类,实现不同层次知识的挖掘。针对最广义层次的文本知识挖掘可实现各主题事务划分,针对子级分类数据的文本知识挖掘可发现下一层次主题分类,针对自定义层次的文本知识挖掘可发现该事件中存在的具体细节。对诉求实际数据的分析结果表明,该方法可在所有诉求数据中挖掘出各种诉求主题,精确挖掘出其中的细节问题,为管理者提供数据和决策支持,提高服务效率。