Journal of Intelligent & Fuzzy Systems - Volume 27, issue 6

Authors: Shekarian, Ehsan | Glock, Christoph H. | Amiri, Seyyed Mehrdad Pourmousavi | Schwindl, Kurt

Article Type: Research Article

Abstract: This paper develops a lot size model for a single-stage production system producing defective items that need to be reworked. Because the rate of defectives and the demand rate are usually not known precisely in practice, we fuzzify both rates with the help of triangular fuzzy numbers. The fuzzified total cost function, which considers setup, inventory carrying, and processing costs is defuzzified using two popular defuzzifying techniques, namely the signed distance and the graded mean integration representation (GMIR) methods. For the defuzzified total cost function, optimal lot sizes are calculated. A numerical example is then provided to illustrate the results …of the model, and the results that were obtained by the two defuzzification methods are compared. The results indicate that the optimal lot size obtained by the signed distance method is larger than the one obtained by the GMIR method. In addition, the results show that the total costs obtained using the GMIR method are higher than those obtained by the signed distance method. Show more

Keywords: Lot sizing, fuzzy set theory, graded mean integration representation method, signed distance method, rework process, single-stage production system

DOI: 10.3233/IFS-141264

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3067-3080, 2014

Authors: Suresh, M. | Vengataasalam, S. | Arun Prakash, K.

Article Type: Research Article

Abstract: Ranking fuzzy numbers plays a very important role in the decision process, data analysis and applications. The concept of an intuitionistic fuzzy number (IFN) is of importance for quantifying an ill-known quantity, and the ranking of IFNs is a very difficult problem. In this paper ranking of triangular intuitionistic fuzzy numbers (TIFNs) is made by means of magnitude and applied to solve intuitionistic fuzzy linear programming problem. Numerical examples are examined to demonstrate the implementation of ranking method.

Keywords: Intuitionistic fuzzy set, triangular intuitionistic fuzzy number, ranking of triangular intuitionistic fuzzy numbers, intuitionistic fuzzy linear programming problem

DOI: 10.3233/IFS-141265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3081-3087, 2014

Authors: Kar, Sukhendu | Purkait, Sudipta

Article Type: Research Article

Abstract: The notion of k-regularity in a semiring generalizes the notion of a regular ring introduced by J. Von Neumaan [15]. In semiring theory, the notion of k-regularity was extensively studied by Sen and Bhuniya [17]. In this paper, we study the concept of k-regularities of a semiring in fuzzy setting and characterize some k-regularities of semirings by using different fuzzy ideals of semirings. We also introduce the concept of power fuzzy semirings and study the notion of k-regularities of power fuzzy semirings with the help of regularities of semigroups.

Keywords: Power fuzzy semiring, fuzzy generalized k-bi-ideal, fuzzy interior k-ideal, k-regular semiring, k-intra-regular semiring

DOI: 10.3233/IFS-141266

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3089-3101, 2014

Authors: Saleh, Sadreddin | Mohammadi, Sirus | Rostami, Mohammad-Amin | Askari, Mohammad-Reza

Article Type: Research Article

Abstract: This paper aims to propose a novel hybrid intelligent linear-nonlinear load forecasting model which takes into account both linearity and nonlinearity of load time series as a requirement of precise forecasting. The linear part of the time series is forecasted by the Auto Regressive Integrated Moving Average (ARIMA). The nonlinear ARIMA residuals are then modeled by the Support Vector Regression (SVR) forecaster. Since the ARIMA residuals tend to be nonlinear thus the proposed methodology tries to subdue these nonlinearities by utilizing the discrete wavelet transform in which the ARIMA residuals are decomposed into their high and low frequency components. In …order to optimize the value of SVR parameters a new Modified Honey Bee Mating Optimization (MHBMO) algorithm is proposed as well. The proposed MHBMO algorithm prevents the optimization process from trapping in local optimums through a new modification phase. The veracity of the proposed methodology is corroborated by applying it to the empirical load data of Fars Electric Power Company, Iran. Show more

Keywords: Support Vector Regression (SVR), Modified Honey Bee Mating Optimization (MHBMO), Short Term Load Forecasting (STLF)

DOI: 10.3233/IFS-141267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3103-3110, 2014

Authors: Nguyen, Dzung Dinh | Ngo, Long Thanh | Watada, Junzo

Article Type: Research Article

Abstract: Multiplex Fluorescent In Situ Hybridization (M-FISH) is a multi-channel chromosome image generating technique that allows colors of the human chromosomes to be distinguished. In this technique, all chromosomes are labelled with 5 fluors and a fluorescent DNA stain called DAPI (4 in, 6-Diamidino-2-phenylindole) that attaches to DNA and labels all chromosomes. Therefore, a M-FISH image consists of 6 images, and each image is the response of the chromosome to a particular fluor. In this paper, we propose a genetic interval type-2 fuzzy c-means (GIT2FCM) algorithm, which is developed and applied to the segmentation and classification of M-FISH images. Chromosome pixels …from the DAPI channel are segmented by GIT2FCM into two clusters, and these chromosome pixels are used as a mask for the remaining five channels. Then, the GIT2FCM algorithm is applied to classify the chromosome pixels into 24 classes, which correspond to the 22 pairs of homologous chromosomes and two sexual chromosomes. The experiments performed using the M-FISH dataset show the advantages of the proposed algorithm. Show more

Keywords: Type-2 fuzzy C-neans clustering, genetic algorithms, MFISH, image segmentation

DOI: 10.3233/IFS-141268

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3111-3122, 2014

Authors: Chen, Hung-Yi

Article Type: Research Article

Abstract: The molten steel level control of strip casting process has the properties of nonlinear uncertainty and time-varying characteristics, hence, it is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. This study develops a hybrid model-free adaptive fuzzy and neural network controller (HAFNC) which combines an adaptive rule with fuzzy and neural network control to overcome the difficulty. The proposed control strategy has online learning ability for responding to the system's nonlinear and time-varying behaviors during the molten steel level control. Since this model-free controller has simple control structure and small number …of control parameters, it is easy to implement. Numerical results based on semi-experimental system dynamic model and parameters are executed to show the control performance of the proposed intelligent controller. Show more

Keywords: Molten steel level control, strip casting process, hybrid adaptive fuzzy and neural network control

DOI: 10.3233/IFS-141269

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3123-3130, 2014

Authors: Khan, Sajid Ali | Hussain, Ayyaz | Usman, Muhammad | Nazir, Muhammad | Riaz, Naveed | Mirza, Anwar Majid

Article Type: Research Article

Abstract: Face recognition has received enormous fame in the field of pattern recognition and computer vision. Being a demanding area intensive research has been done by many researchers for more than a decade. However no standard technique exists for extracting the significant features of facial images in different categories. Techniques found in literature produces high accuracy but are computationally expensive which are not applicable in real time applications. In this paper, two well known methods, Discrete Wavelet Transform (DWT) and Weber Local Descriptor (WLD) are used to extract the face discriminative features. First for both types of features, the recognition accuracy …is separately measured. In the next step, both types of features are fused using the concatenation method to improve the accuracy rate. To select more discriminative features and reduce data dimensions, computationally efficient algorithm (Kruskal-Wallis) is used. In the last step, three classifiers (SVM, KNN and BPNN) ensemble to improve the accuracy rate. Proposed technique is more efficient in terms of time complexity as compared to GA and PSO. Yale face database is used for all experiments. The proposed technique is highly robust to facial variations like occlusion, illumination and expression change and computationally efficient as compared to existing methods. Show more

Keywords: Face recognition, feature fusion, weber local descriptor, feature selection, feature fusion

DOI: 10.3233/IFS-141270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3131-3143, 2014

Authors: Rouhani, Seyed Hossein | Sheikholeslami, Abdolreza | Ahmadi, Roya | Hosseini, Hossein

Article Type: Research Article

Abstract: In power system any unpredictable variations of consumption load will cause fluctuation in frequency and the transferred power between the lines. Load frequency control is one of the main problems in the power system which synchronizes with automatic generation control. Checking the different factors that cause unbalance in power system is necessary and one of the important factors of them is the load that randomly varies. In this paper, a three area power system in deregulated environments with participation of synchronized generation of wind with Doubly Fed Induction Generator (DFIG), hydro and thermal power plants are studied. The load in …this power system is considered as a random variable which periodically and randomly has frequency of 0.25 around nominal amount. Fluctuations are caused due to considering load as a random variable and using wind-powered turbine with DFIG in power system. To improve the response of power system, the optimal fuzzy logic, PID controller and FACTS devices will be used. Imperialist Competitive Algorithm (ICA) has been used for optimizing the parameters of a fuzzy logic controller. PID Controller parameters have been optimized with three intelligent algorithms, ICA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm and the results will be compared with each other. With comparison the result it is observed that in the case of using optimal fuzzy logic controller (OFLC) we have the fastest response with the minimum over shoot and under shoot, also in this case the transferred power between the areas is settled in the lowest value. The results show a significant reduction in losses in transmission lines. Show more

Keywords: Deregulated environments, random variable load (RVL), wind-powered turbine with DFIG, load frequency control (LFC), optimal fuzzy logic controller (OFLC), intelligent algorithm

DOI: 10.3233/IFS-141271

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3145-3157, 2014

Authors: Hesamian, Gholamreza | Chachi, Jalal

Article Type: Research Article

Abstract: This paper extends the non-parametric Sign test to the case when available observations and underlying hypotheses about the population median are imprecise quantities. To do this, by using some elements of possibility theory, we suggest a ranking index among imprecise observations. The index is applied to extend the usual concepts of classical hypothesis testing problem to obtain Sing test statistic and interval p-value. Comparing p-value and a nominal significance level, the fuzzy non-parametric Sign test is constructed providing some degrees to accept or reject the imprecise hypotheses. Several numerical examples are provided throughout the paper to clarify the proposed approach. …Finally, a comprehensive review of several well-known non-parametric test approaches is given. Show more

Keywords: Fuzzy sign test, imprecise hypotheses, imprecise observations, interval p-value, necessity and possibility indices

DOI: 10.3233/IFS-141272

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3159-3167, 2014

Authors: Ghofrani, Fatemeh | Helfroush, Mohammad Sadegh | Danyali, Habibollah | Kazemi, Kamran

Article Type: Research Article

Abstract: This paper proposes a novel approach for medical x-ray image classification using fuzzification of Contourlet-based Center Symmetric Local Binary Patterns (CCS-LBPs). The proposed classification method consists of three stages. In the first stage, local features are obtained by partitioning each image into 25 overlapping sub-images, computing the 2-level contourlet transform of each subimage and extracting CS-LBPs from each resulting subband. In the second stage, fuzzy logic using reduced CCS-LBPs is employed to determine the degree of membership of subimages to each class. Finally, in order to assign images to their respective classes, we utilize membership values as the input of …classifiers such as support vector machine (SVM) and k-nearest neighbor (K-NN). This work makes a major contribution to improve the performance of these classifiers. We conducted experiments on a subset of IRMA dataset to evaluate the effectiveness of our classification scheme. Experimental results reveal that the proposed scheme not only achieves a very good performance but also learns well even with a small number of training images. Show more

Keywords: Medical x-ray images, local binary patterns, contourlet transform, fuzzy membership, image classification

DOI: 10.3233/IFS-141273

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3169-3180, 2014