A hybrid SVM and supervised learning approach to fuzzy min‐max hyperbox classifiers

Abstract

In this paper, a fuzzy min‐max hyperbox classifier is designed to solve M‐class classification problems using a hybrid SVM and supervised learning approach. In order to solve a classification problem, a set of training patterns is gathered from a considered classification problem. However, the training set may include several noisy patterns. In order to delete the noisy patterns from the training set, the support vector machine is applied to find the noisy patterns so that the remaining training patterns can describe the behavior of the considered classification system well. Subsequently, a supervised learning method is proposed to generate fuzzy min‐max hyperboxes for the remaining training patterns so that the generated fuzzy min‐max hyperbox classifier has good generalization performance. Finally, the Iris data set is considered to demonstrate the good performance of the proposed approach for solving this classification problem.     

A novel fuzzy group decision-making approach to prioritising engineering characteristics in QFD under uncertainties

In new product development, design teams commonly need to define engineering characteristics (ECs) in a quality function deployment (QFD) planning process. Prioritising the engineering characteristics in QFD is essential to properly plan resource allocation. However, the inherent vagueness or impreciseness in QFD presents a special challenge to the effective calculation of the importance of ECs. Generally, there are two types of uncertain input in the QFD process: human perception and customer heterogeneity. Many contributions have been made on methods to prioritise ECs. However, most previous studies only address one of the two types of uncertainties that could affect the robustness of prioritising ECs. To address the two types of uncertainties simultaneously, a novel fuzzy group decision-making method that integrates a fuzzy weighted average method with a consensus ordinal ranking technique is proposed. An example is presented to illustrate the effectiveness of the proposed approach. Results of the implementation indicate that the robustness of prioritising ECs based on the proposed approach is better than that based on the method of Chen et al. (Chen, Y., Fung, R.Y.K., Tang, J.F., 2006. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174 (3), 1553–1556).     

A design of fast‐locking without overshoot frequency‐locked servo system by fuzzy control technique

Abstract

In this paper, a Fuzzy Pulse Pump Controller (FPPC) is proposed to realize a Fuzzy‐Controlled Frequency‐Locked Servo system (FC‐FLS) for getting a fast locking response without overshoot. A prototype FC‐FLS is designed and built to assess the system performance. In comparison with the Frequency Pump Controller‐based FLS (FPC‐FLS) and Variable Slope Pulse Pump Controller‐based FLS (VSPPC‐FLS), the acquisition times of the FC‐FLS are improved over 40%. In particular, there is no overshoot in the FC‐FLS for any servo distance. This means that a fast‐locking FLS, without overshoot, has been successfully implemented as theoretical prediction.     

A new method to deal with fuzzy classification problems by tuning membership functions for fuzzy classification systems

Abstract

This paper presents a new method to construct and tune membership functions and generate fuzzy classification rules from training instances for handling the Iris data classification problem. First, we find two attributes of the Iris data from the training instances that are suitable to serve as classification criteria. Then, we construct and tune the membership functions of these two attributes and generate fuzzy classification rules from the training instances. The proposed method generates the same number of fuzzy classification rules as the number of species of the training instances. It generates fewer fuzzy classification rules and can get a higher average classification accuracy rate than the existing methods.     

Earlier detection of cancer regions from MR image features and SVM classifiers

In this article, we examine the use of several segmentation algorithms for medical image classification. This work detects the cancer region from magnetic resonance (MR) images in earlier stage. This is accomplished in three stages. In first stage, four kinds of region‐based segmentation techniques are used such as K‐means clustering algorithm, expectation–maximization algorithm, partial swarm optimization algorithm, and fuzzy c‐means algorithm. In second stage, 18 texture features are extracting using gray level co‐occurrence matrix (GLCM). In stage three, classification is based on multi‐class support vector machine (SVM) classifier. Finally, the performance analysis of SVM classifier is analyzed using the four types of segmentation algorithm for a group of 200 patients (32—Glioma, 32—Meningioma, 44—Metastasis, 8—Astrocytoma, 72—Normal). The experimental results indicate that EM is an efficient segmentation method with 100% accuracy. In SVM, quadratic and RBF (σ = 0.5) kernel methods provide the highest classification accuracy compared to all other SVM kernel methods. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 196–208, 2016     

Stable fuzzy control with adaptive rotor imbalance compensation for nonlinear magnetic bearing systems

Abstract

The six‐DOF nonlinear dynamics and rotor mass‐imbalance induced vibration of a high‐speed magnetic bearing are rather complex. In this paper, an analytical T‐S (Takagi‐Sugeno) fuzzy model with simplified linearly‐parameterized mass‐imbalance model for magnetic bearings is first constructed. Based on the T‐S fuzzy model, a stable fuzzy control including adaptive imbalance compensation is then proposed through robust performance analysis. Simulation validations show that the proposed control law can suppress the rotor imbalance‐induced vibration and has excellent capability for high‐speed tracking and levitation control.     

Development of Run-To-Run (R2R) controller for the multiple-input multiple-output (MIMO) system using fuzzy control theories

Run-to-Run (R2R) control has been extensively applied in semiconductor manufacturing. In particular, del Castillo, E. and Rajagopal, R., A multivariate double EWMA process adjustment scheme for drifting processes. IIE Trans., 2002, 34, 1055–1068, investigated double multivariate exponentially weighted moving average (MEWMA) controller for the multiple-input multiple-output (MIMO) system in an attempt to adjust and maintain the linear dynamic process outputs on target. Multivariate fuzzy control, inherently different from conventional MEWMA-based control, is another promising alternative that consists of fuzzy logic and set concept. Providing the fuzzy control can structure an appropriate membership function for the R2R MIMO system, thus it can be shown a practically useful control tool in comparison to MEWMA control. In this paper, fuzzy logic is utilized to design the multivariate fuzzy controller for the type of R2R applications based primarily on the min-max-gravity method advocated by Gupta, M.M., Kiszka, J.B. and Trojan, G.M., Multivariable structure of fuzzy control systems. IEEE Trans. Sys., Man Cybern., 1986, 16, 638–656. Under a variety of disturbance models, the proposed multivariate fuzzy controller can produce quite competitive control performance when compared to MEWMA control.     

Comparisons on discrete‐time modeling techniques of DC‐DC switching converters

Abstract

The performance and effectiveness of several discrete‐time modeling techniques of dc‐dc switching converters, including new ones devised in this paper, are compared and evaluated. It is shown in this paper that the latest bilinear discrete‐time modeling method by Rajasekaran et al., 2003 suffers in both steady state and transient analysis due to the bilinear approximation of the original mathematical model. The proposed alternative discrete‐time models are obtained by using the well‐known Cayley‐Hamilton theorem, and they are shown, via examples, to be superior to the previous results.     

Integrating a decomposition procedure with problem reduction for factory scheduling with disruptions: a simulation study

Dispatching rules are widely used in industry because schedules obtained from optimization procedures can be difficult to implement in the face of executional uncertainties. Barua et al. (Barua, A., Narasimhan, R., Upasani, A. and Uzsoy, R., Implementing global factory schedules in the face of stochastic disruptions. Int. J. Prod. Res., 2005, 43(4), 793–818) implement global schedules obtained from an optimization-based heuristic using a dispatching rule, and outperform myopic dispatching rules in the face of disruptions. However, the computation of the global schedules is still time-consuming for realistic instances. Upasani et al. (Upasani, A., Uzsoy, R. and Sourirajan, K., A problem reduction approach for scheduling semiconductor wafer fabrication facilities. IEEE Trans. Semicon. Manuf., 2006, 19, 216–225) develop a problem reduction scheme based on load disparity between work centres, and report significant reduction in CPU times with minimal loss of solution quality in deterministic experiments. In this paper we integrate the problem-reduction scheme to obtain global schedules with the dispatching approach of Barua et al. (Barua, A., Narasimhan, R., Upasani, A. and Uzsoy, R., Implementing global factory schedules in the face of stochastic disruptions. Int. J. Prod. Res., 2005, 43(4), 793–818) in a multi-product environment with stochastic machine breakdowns and job arrivals. A simulation model of a scaled-down wafer fabrication facility is used to evaluate the performance of the proposed procedures. Results show that the integrated procedure outperforms the benchmark dispatching rules while significantly reducing computation times.     

A note on “an improved MCDM DEA model for technology selection”

This paper gives a modification on the improved technology selection DEA model proposed by Amin et al. (Amin, G.R., Toloo, M. and Sohrabi, B., An improved MCDM DEA model for technology selection. Int. J. Prod. Res., 2006, 44, 2681–2686). The paper shows the problem of using the existing model and then introduces a new modified one to obtain a single efficient DMU for the technology selection alternatives.     

A fuzzy AHP-based performance evaluation model for implementing SPC in the Taiwanese LCD industry

Statistical process control (SPC) is one of the most practical and widely used tools to enhance product quality and reduce costs. However, the implementation of SPC has often resulted in unsatisfactory performance; moreover, there is no well-established standard for evaluation of the results of introducing the system. The present study addresses this problem by proposing an effective and convenient performance-evaluation model for implementing SPC. The proposed model draws on the DMAIC methodology of Six Sigma, the performance-evaluation model of Lin et al. (Lin, W.T., Liu, C.H., Hsu, I.C., and Lai, C.T., 2004. An empirical study of QS 9000 in the automobile and related industries in Taiwan. Total Quality Management, 15 (3), 335–378), and the fuzzy mathematical programming of Kaufmann and Gupta (Kaufmann, A. and Gupta, M.M., 1991. Introduction to fuzzy arithmetic: theory and application. New York: Van Nostrand Reinhold) to define the fuzzy indices and control values of importance, action, and performance in developing the proposed performance-evaluation model. The model is then applied in a case study of a Taiwanese liquid crystal display manufacturer. A questionnaire is designed to establish fuzzy indices of importance, action, and performance values for assessment by analytic hierarchy process methodology. Various critical factors and feasible improvement strategies are then compared (using computed weights) to determine the priority of the improvement strategies. To verify improvement, the same model is used to re-evaluate system control performance after implementing the improvement strategies for some time. The study demonstrates that the proposed model is an effective and convenient tool that can be used to analyse and improve the performance of an existing SPC system or to enhance success in implementing a new SPC system while working within constraints of time and costs.     

Combining classifiers through fuzzy cognitive maps in natural images

A new automatic hybrid classifier for natural images by combining two base classifiers through the fuzzy cognitive maps (FCMs) approach is presented in this study. The base classifiers used are fuzzy clustering (FC) and the parametric Bayesian (BP) method. During the training phase, different partitions are established until a valid partition is found. Partitioning and validation are two automatic processes based on validation measurements. From a valid partition, the parameters of both classifiers are estimated. During the classification phase, FC provides for each pixel the supports (membership degrees) that determine which cluster the pixel belongs to. These supports are punished or rewarded based on the supports (probabilities) provided by BP. This is achieved through the FCM approach, which combines the different supports. The automatic strategy and the combined strategy under the FCM framework make up the main findings of this study. The analysis of the results shows that the performance of the proposed method is superior to other hybrid methods and more accurate than the single usage of existing base classifiers.     

Fuzzy adaptive preventive maintenance in a manufacturing control system: a step towards self-maintenance

In this paper, a framework of integrating preventive maintenance (PM) and manufacturing control system is proposed. Fuzzy-logic control is used to enable an intelligent approach of integrating PM and a manufacturing control system. This will contribute to the novel development of an integrated and intelligent framework in those two fields that are sometimes difficult to achieve. This idea is based on combining work on an intelligent real-time controller for a failure-prone manufacturing system using a fuzzy-logic approach (Yuniarto, M.N. and Labib, A.W., Optimal control system of an unreliable machine using fuzzy logic control: from design to implementation. Int. J. Prod. Res. (in press a); Yuniarto, M.N. and Labib, A.W., Intelligent real time control of disturbances in manufacturing systems. Integr. Manuf. Syst.: Int. J. Manuf. Technol. Manage. (in press b) and the work on PM proposed by Labib et al. (Labib, A.W., Williams, G.B. and O’Connor, R.F., An intelligent maintenance model (system): an application of analytic hierarchy process and a fuzzy logic rule-based controller. J. Oper. Res. Soc., 1998, 49, 745–757)). The aim of the research is to control a failure-prone manufacturing system and at the same time propose which PM method is applicable to a specific failure-prone manufacturing system. The mean time to repair and mean time between failures of the system are used as integrator agents, by using them to couple the two areas to be integrated (i.e. a maintenance system and manufacturing system).     

基于IITD模糊熵与随机森林的滚动轴承故障诊断方法

针对滚动轴承故障微弱振动信号特征提取后难以识别的问题,提出基于改进的固有时间尺度分解(IITD)和模糊熵(FE)输入随机森林(RF)模式识别的滚动轴承故障诊断方法.首先,利用轴承试验台采集正常、滚动体故障、内圈故障、外圈故障等4种状态下轴承的振动信号;通过IITD分解将采集到的振动信号分解成一组固有旋转分量(PRC),...     

Linear defuzzified output of fuzzy controllers via mixed fuzzy logic operators

Abstract

The linear defuzzified output of a fuzzy controller with two fuzzy variable inputs and one output is discussed in this paper. Arbitrary amounts of triangular fuzzy numbers are employed to fuzzify the linguistic variables in fuzzy control rules. We show that the defuzzified output is exactly equivalent to a linear function of the inputs to the fuzzy controller by using three mixed fuzzy logic operators to evaluate the control rules.     

Ranking alternatives via maximizing set and minimizing set based fuzzy MCDM approach

Abstract

This work suggests a maximizing set and minimizing set based fuzzy multiple criteria decision‐making (MCDM) model, where criteria are classified into cost and benefit criteria. The final fuzzy evaluation value of each alternative is developed based on the concept of subtracting the summation of weighted normalized benefit ratings from that of weighted normalized cost ratings. Using interval arithmetic of fuzzy numbers can develop the membership functions for the final fuzzy evaluation values. Chen's maximizing set and minimizing set is then applied to defuzzify all the final fuzzy numbers for ranking alternatives. Formulas for the membership functions and ranking procedure of the final fuzzy numbers are clearly presented. The suggested method provides an extension to the fuzzy MCDM techniques available. A numerical example demonstrates the computational process of the proposed method.     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

IP交换中模糊流分类器的研究

对ＩＰ交换中长流、短流分类时应考虑的各种因素进行了分析,提出了一种模糊流分类器,它可根据网络中流的具体情况和ＩＰ交换机的工作状况,自动调节流的分类阈值。与常规Ｘ／Ｙ分类器相比,模糊分类器能更有效地利用ＩＰ交换机的资源,交换更多的数据包。     

Decision making of project under fuzzy information

Abstract

In this paper, fuzzy decision of resource‐constrained project planning are made. The resources are classified into renewable and non‐renewable types.

The activities in the project may have alternative modes depending upon the dispatching, subcontracting, new techniques employed, etc. Thus, the resource requirements for each mode are different. Also, the duration of each mode is assumed to have a fuzzy character.

Based on accepted risk level and the multi‐objective management perspectives (completion time and cost), the mode suitable for each activity to perform the project is decided by the preliminary screening, feasible screening and priority making stages. During the analysis, the risk level is defined by α‐cuts of completion time and cost. In the preliminary screening stage, the candidates of mode for each activity are chosen by the time limitation in the critical path and the requirements of non‐renewable resource. In the feasible screening stages, the accepted mode‐combination of the activities are chosen by the requirements of the renewable resources and by the completion time. Then the priority is decided by the fuzzy ordering in the final stage.

Typical examples at different accepted risk levels are presented.     

Improved fuzzy entropy clustering algorithm for MRI brain image segmentation

Magnetic resonance imaging (MRI) brain image segmentation is essential at preliminary stage in the neuroscience research and computer‐aided diagnosis. However, presence of noise and intensity inhomogeneity in MRI brain images leads to improper segmentation. The fuzzy entropy clustering (FEC) is often used to deal with noisy data. One major disadvantage of the FEC algorithm is that it does not consider the local spatial information. In this article, we have proposed an improved fuzzy entropy clustering (IFEC) algorithm by introducing a new fuzzy factor, which incorporates both local spatial and gray‐level information. The IFEC algorithm is insensitive to noise, preserves the image detail during clustering, and is free of parameter selection. The efficacy of IFEC algorithm is demonstrated by comparing it quantitatively with the state‐of‐the‐art segmentation approaches in terms of similarity index on publically available real and simulated MRI brain images.