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Yan, Xiaoan, Yadong Xu, and Minping Jia. "Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy." Entropy 23, no. 9 (August 30, 2021): 1128. http://dx.doi.org/10.3390/e23091128.
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The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.
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Pavlačka, Ondřej. "Note on the lack of equality between fuzzy weighted average and fuzzy convex sum." Fuzzy Sets and Systems 213 (February 2013): 102–5. http://dx.doi.org/10.1016/j.fss.2012.08.003.
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Wei, Guiwu, and Mao Lu. "Pythagorean Hesitant Fuzzy Hamacher Aggregation Operators in Multiple-Attribute Decision Making." Journal of Intelligent Systems 28, no. 5 (October 17, 2017): 759–76. http://dx.doi.org/10.1515/jisys-2017-0106.
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Abstract The Hamacher product is a t-norm and the Hamacher sum is a t-conorm. They are good alternatives to the algebraic product and the algebraic sum, respectively. Nevertheless, it seems that most of the existing hesitant fuzzy aggregation operators are based on algebraic operations. In this paper, we utilize Hamacher operations to develop some Pythagorean hesitant fuzzy aggregation operators: Pythagorean hesitant fuzzy Hamacher weighted average operator, Pythagorean hesitant fuzzy Hamacher weighted geometric operator, Pythagorean hesitant fuzzy Hamacher ordered weighted average operator, Pythagorean hesitant fuzzy Hamacher ordered weighted geometric operator, Pythagorean hesitant fuzzy Hamacher hybrid average operator, and Pythagorean hesitant fuzzy Hamacher hybrid geometric operator. The prominent characteristics of these proposed operators are studied. Then, we utilize these operators to develop some approaches for solving the Pythagorean hesitant fuzzy multiple-attribute decision-making problems. Finally, a practical example is given to verify the developed approach and to demonstrate its practicality and effectiveness.
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Cui, Dong, Jinhuan Wang, Zhijie Bian, Qiuli Li, Lei Wang, and Xiaoli Li. "Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus." Journal of Innovative Optical Health Sciences 08, no. 05 (August 21, 2015): 1550010. http://dx.doi.org/10.1142/s1793545815500108.
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EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment (MCI) in T2DM. To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM, six entropies combining empirical mode decomposition (EMD), including Approximate entropy (ApEn), Sample entropy (SaEn), Fuzzy entropy (FEn), Permutation entropy (PEn), Power spectrum entropy (PsEn) and Wavelet entropy (WEn) were used in the study. A feature extraction technique based on maximization of the area under the curve (AUC) and a support vector machine (SVM) were subsequently used to for features selection and classification. Finally, Pearson's linear correlation was employed to study associations between these entropies and cognitive functions. Compared to other entropies, FEn had a higher classification accuracy, sensitivity and specificity of 68%, 67.1% and 71.9%, respectively. Top 43 salient features achieved classification accuracy, sensitivity and specificity of 73.8%, 72.3% and 77.9%, respectively. P4, T4 and C4 were the highest ranking salient electrodes. Correlation analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7, F8 and P4, and PsEn based on EMD was positively correlated to Montreal cognitive assessment (MoCA) and memory at electrode T4. In sum, FEn based on EMD in right-temporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM.
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Li, Yanping, Qi Wang, Tao Wang, Jian Pei, and Shuo Zhang. "Feature Extraction of EEG Signals Based on Local Mean Decomposition and Fuzzy Entropy." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (September 13, 2020): 2058017. http://dx.doi.org/10.1142/s0218001420580173.
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An improved feature extraction method is proposed aiming at the recognition of motor imagined electroencephalogram (EEG) signals. Using local mean decomposition, the algorithm decomposes the original signal into a series of product function (PF) components, and meaningless PF components are removed from EEG signals in the range of mu rhythm and beta rhythm. According to the principle of feature time selection, 4[Formula: see text]s to 6[Formula: see text]s motor imagery EEG signals are selected as classification data, and the sum of fuzzy entropies of second-and third-order PF components of [Formula: see text], [Formula: see text] lead signals is calculated, respectively. Mean value of fuzzy entropy [Formula: see text] is used as input element to construct EEG feature vector, and support vector machine (SVM) is used to classify and predict EEG signals for recognition. The test results show that this feature extraction method has higher classification accuracy than the empirical mode decomposition method and the total empirical mode decomposition method.
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Zhu, Yahui, and Li Gao. "Decision Method of Probabilistic Hesitant Fuzzy Information Based on Hamacher Aggregation Operators and MULTIMOORA." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 6 (December 2020): 1361–69. http://dx.doi.org/10.1051/jnwpu/20203861361.
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Aiming at solving the problem of probability hesitation fuzzy multi-attribute decision making, a new decision-making method of probability hesitation fuzzy multi-attribute is proposed in this paper, based on Hamacher operations and MULTIMOORA method. Firstly, probability hesitation fuzzy Hamacher operations are defined, including sum, product, scalar multiplication and exponentiation, and their properties are studied. On this basis, probability hesitation fuzzy Hamacher weighted average operator and probability hesitation fuzzy Hamacher weighted geometric average operator are proposed, and their properties are also studied. Secondly, alternative from multiple perspectives are chosen and compared by using the MULTIMOORA method. Finally, the effectiveness and feasibility of the decision-making method are verified by an example.
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Ganesh Kumar, Vinod Jangid, Gaurav Sharma,. "Strategic Management in Marketing: A Game Theoretic Approach." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 5, 2021): 2518–24. http://dx.doi.org/10.17762/turcomat.v12i6.5697.
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In marketing, a real-world dilemma emerging between two rivals, McDonald's and Burger King, is investigated. Both firms use three strategies: discounted pricing, status quo, and aggressive commercial. In such cases, ambiguity is a determining factor. To deal with confusion in payoffs, octagonal fuzzy numbers are used. To rank fuzzy numbers, the average of odd positions, average of even positions, and quartile deviations are used. To solve the reduced modelled two competitors zero sum fuzzy matrix games, the proposed ranking methods are used. Finally, the findings are compared to current approaches that are quite similar to the proposed approach.
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Madarro-Capó, Evaristo José, Carlos Miguel Legón-Pérez, Omar Rojas, and Guillermo Sosa-Gómez. "Information Theory Based Evaluation of the RC4 Stream Cipher Outputs." Entropy 23, no. 7 (July 14, 2021): 896. http://dx.doi.org/10.3390/e23070896.
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This paper presents a criterion, based on information theory, to measure the amount of average information provided by the sequences of outputs of the RC4 on the internal state. The test statistic used is the sum of the maximum plausible estimates of the entropies H(jt|zt), corresponding to the probability distributions P(jt|zt) of the sequences of random variables (jt)t∈T and (zt)t∈T, independent, but not identically distributed, where zt are the known values of the outputs, while jt is one of the unknown elements of the internal state of the RC4. It is experimentally demonstrated that the test statistic allows for determining the most vulnerable RC4 outputs, and it is proposed to be used as a vulnerability metric for each RC4 output sequence concerning the iterative probabilistic attack.
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Wang, Ping, Guiwu Wei, Jie Wang, Rui Lin, and Yu Wei. "Dual Hesitant q-Rung Orthopair Fuzzy Hamacher Aggregation Operators and their Applications in Scheme Selection of Construction Project." Symmetry 11, no. 6 (June 6, 2019): 771. http://dx.doi.org/10.3390/sym11060771.
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The q-rung orthopair fuzzy set (q-ROFS), which is the extension of intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS), satisfies the sum of q-th power of membership degree and nonmembership degree is limited 1. Evidently, the q-ROFS can depict more fuzzy assessment information and consider decision-maker’s (DM’s) hesitance. Thus, the concept of a dual hesitant q-rung orthopair fuzzy set (DHq-ROFS) is developed in this paper. Then, based on Hamacher operation laws, weighting average (WA) operator and weighting geometric (WG) operator, some dual hesitant q-rung orthopair fuzzy Hamacher aggregation operators are developed, such as the dual hesitant q-rung orthopair fuzzy Hamacher weighting average (DHq-ROFHWA) operator, the dual hesitant q-rung orthopair fuzzy Hamacher weighting geometric (DHq-ROFHWG) operator, the dual hesitant q-rung orthopair fuzzy Hamacher ordered weighted average (DHq-ROFHOWA) operator, the dual hesitant q-rung orthopair fuzzy Hamacher ordered weighting geometric (DHq-ROFHOWG) operator, the dual hesitant q-rung orthopair fuzzy Hamacher hybrid average (DHq-ROFHHA) operator, and the dual hesitant q-rung orthopair fuzzy Hamacher hybrid geometric (DHq-ROFHHG) operator. The precious merits and some particular cases of above mentioned aggregation operators are briefly introduced. In the end, an actual application for scheme selection of construction project is provided to testify the proposed operators and deliver a comparative analysis.
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Savarimuthu, Charles, and Arockiam L. "Pairwise Fuzzy Ordered Weighted Average Algorithm-Gaussian Mixture Model for Feature Reduction." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, no. 1 (May 30, 2013): 287–301. http://dx.doi.org/10.24297/ijct.v6i1.4457.
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Feature Reduction is a kind of dimensionality reduction of feature space. There are a number of approaches are used to identify the significant features but they are not using the weighing approach. The weighing approach is quite useful for obtaining the significant features and removing the insignificant and irrelevant features using OWA formulation. The aim of this approach is to obtain the significant features and removing insignificant features by using the pairwise approach. This approach is helpful to find the weights of pairwise features at the same time, which leads to remove the insignificant features from the feature space using OWA. The significance of the OWA formulation is that, the paired features are identified in priori and their sum of weights are equal to 1. OWA criterion is introduced to obtain the significant features that are useful for predicting the accuracy of the cluster in GMM.
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Pasila, Felix, Ajoy K. Palit, and Georg Thiele. "Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 4 (July 20, 2008): 361–69. http://dx.doi.org/10.20965/jaciii.2008.p0361.
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The paper describes a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with additional moving average filter is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low SSE value with given training data of neuro-fuzzy network, are further fine tuned during the network training. Finally, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of electrical load time series.
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BAR-YAM, Y. "MULTISCALE COMPLEXITY/ENTROPY." Advances in Complex Systems 07, no. 01 (March 2004): 47–63. http://dx.doi.org/10.1142/s0219525904000068.
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We discuss the role of scale dependence of entropy/complexity and its relationship to component interdependence. The complexity as a function of scale of observation is expressed in terms of subsystem entropies for a system having a description in terms of variables that have the same a priori scale. The sum of the complexity over all scales is the same for any system with the same number of underlying degrees of freedom (variables), even though the complexity at specific scales differs due to the organization/interdependence of these degrees of freedom. This reflects a tradeoff of complexity at different scales of observation. Calculation of this complexity for a simple frustrated system reveals that it is possible for the complexity to be negative. This is consistent with the possibility that observations of a system that include some errors may actually cause, on average, negative knowledge, i.e. incorrect expectations.
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Wibawati, Muhammad Mashuri, Purhadi, and Irhamah. "A Fuzzy Bivariate Poisson Control Chart." Symmetry 12, no. 4 (April 5, 2020): 573. http://dx.doi.org/10.3390/sym12040573.
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In the present paper, we develop a fuzzy bivariate Poisson (FBP) control chart based on a fuzzy c chart. The FBP chart is used to monitor the sum of the nonconformities of each quality characteristic. There are two contributions of this work. First, we propose a new fuzzy parameter estimation to create a triangular fuzzy number (TFN). Second, our control chart is flexible, because we involve the α c u t to measure the level of tightness of inspection. Furthermore, the statistic of FBP is being able to visualise the monitoring process in a graphical form. In addition, the simulation study indicates that the performance of our proposed chart, based on average run length (ARL), is more sensitive than the performance of a conventional bivariate Poisson (BP) chart. Moreover, an illustration example shows that the FBP chart has relatively more sensitive performance compared to the conventional BP chart.
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Kreinovich, Vladik, Olga Kosheleva, and Songsak Sriboonchitta. "Why Use a Fuzzy Partition in F-Transform?" Axioms 8, no. 3 (August 2, 2019): 94. http://dx.doi.org/10.3390/axioms8030094.
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In many application problems, F-transform algorithms are very efficient. In F-transform techniques, we replace the original signal or image with a finite number of weighted averages. The use of a weighted average can be naturally explained, e.g., by the fact that this is what we get anyway when we measure the signal. However, most successful applications of F-transform have an additional not-so-easy-to-explain feature: the fuzzy partition requirement that the sum of all the related weighting functions is a constant. In this paper, we show that this seemingly difficult-to-explain requirement can also be naturally explained in signal-measurement terms: namely, this requirement can be derived from the natural desire to have all the signal values at different moments of time estimated with the same accuracy. This explanation is the main contribution of this paper.
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Marton, Katalin. "Logarithmic Sobolev inequalities in discrete product spaces." Combinatorics, Probability and Computing 28, no. 06 (June 13, 2019): 919–35. http://dx.doi.org/10.1017/s0963548319000099.
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AbstractThe aim of this paper is to prove an inequality between relative entropy and the sum of average conditional relative entropies of the following form: for a fixed probability measure q on , ( is a finite set), and any probability measure on , (*) $$D(p||q){\rm{\le}}C \cdot \sum\limits_{i = 1}^n {{\rm{\mathbb{E}}}_p D(p_i ( \cdot |Y_1 ,{\rm{ }}...,{\rm{ }}Y_{i - 1} ,{\rm{ }}Y_{i + 1} ,...,{\rm{ }}Y_n )||q_i ( \cdot |Y_1 ,{\rm{ }}...,{\rm{ }}Y_{i - 1} ,{\rm{ }}Y_{i + 1} ,{\rm{ }}...,{\rm{ }}Y_n )),} $$ where pi(· |y1, …, yi−1, yi+1, …, yn) and qi(· |x1, …, xi−1, xi+1, …, xn) denote the local specifications for p resp. q, that is, the conditional distributions of the ith coordinate, given the other coordinates. The constant C depends on (the local specifications of) q.The inequality (*) ismeaningful in product spaces, in both the discrete and the continuous case, and can be used to prove a logarithmic Sobolev inequality for q, provided uniform logarithmic Sobolev inequalities are available for qi(· |x1, …, xi−1, xi+1, …, xn), for all fixed i and fixed (x1, …, xi−1, xi+1, …, xn). Inequality (*) directly implies that the Gibbs sampler associated with q is a contraction for relative entropy.In this paper we derive inequality (*), and thereby a logarithmic Sobolev inequality, in discrete product spaces, by proving inequalities for an appropriate Wasserstein-like distance.
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Plebankiewicz, Edyta, and Damian Wieczorek. "Multidimensional sensitivity study of the fuzzy risk assessment module in the life cycle of building objects." Open Engineering 8, no. 1 (December 26, 2018): 490–99. http://dx.doi.org/10.1515/eng-2018-0059.
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Abstract The main purpose of this paper is to check the correctness of the operation of “the fuzzy risk assessment module in the life cycle of building objects”. The authors examine sensitivity of the module to the possible change of parameters that may affect the final result of the calculations due to modification: sets of membership functions for input and output variables, rules of fuzzy implication to the process of aggregation of premises and aggregation of result conclusions, as well as methods for defuzzification the resultant value. All 99 combinations of input variable values were simulated taking into account the combination for: 4 sets of membership functions (polygonal, complex, harmonic and Gaussian dunctions), 4 sets of T-norms and S-norms (minimum and maximum of Mamdani, algebraic product and sum, product and sum of Hamacher, and product and sum of Einstein) and 2 defuzzification methods (centre of gravity and bisector area method). The authors calculated arithmetic average (m), standard deviation (s) and coefficient of variation (V), which is a relative measure of variation for each of the 3168 combinations. Based on the results of research, the authors recommended the most appropriate sets of parameters that may affect the final result of the calculations.
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Wakakuwa, Eyuri, and Yoshifumi Nakata. "One-Shot Randomized and Nonrandomized Partial Decoupling." Communications in Mathematical Physics 386, no. 2 (July 16, 2021): 589–649. http://dx.doi.org/10.1007/s00220-021-04136-5.
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AbstractWe introduce a task that we call partial decoupling, in which a bipartite quantum state is transformed by a unitary operation on one of the two subsystems and then is subject to the action of a quantum channel. We assume that the subsystem is decomposed into a direct-sum-product form, which often appears in the context of quantum information theory. The unitary is chosen at random from the set of unitaries having a simple form under the decomposition. The goal of the task is to make the final state, for typical choices of the unitary, close to the averaged final state over the unitaries. We consider a one-shot scenario, and derive upper and lower bounds on the average distance between the two states. The bounds are represented simply in terms of smooth conditional entropies of quantum states involving the initial state, the channel and the decomposition. Thereby we provide generalizations of the one-shot decoupling theorem. The obtained result would lead to further development of the decoupling approaches in quantum information theory and fundamental physics.
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Gorbatkov, Stanislav, and Svetlana Farkhieva. "Bankruptcy Risk Assessment in Corporate Lending Based on Hybrid Neural Networks and Fuzzy Models." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 13, no. 1 (November 20, 2019): 28–39. http://dx.doi.org/10.17323/j.jcfr.2073-0438.13.1.2019.28-39.
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The purpose of this article is the presentation of a novel and unconventional algorithm for bankruptcy risk management in banking technologies catered towards lending to legal entities (enterprises and companies). The challenges of assessing risk in this area primarily relate to the reduction of type I and type II errors when making decisions on the terms of lending (i.e. loan amounts and repayment parameters) on the ostensibly objective basis of a borrower’s creditworthiness assessment.
As such, it is necessary to use a unified procedure to select appropriate economic indicators for any bankruptcy model in order to reduce the high degree of uncertainty and noisiness of publicly available databases, and to take into consideration the specific character of knowledge-intensive, high-tech and “green” manufacturing. In order to approach this challenge, a mix of various methods is presented in this article, including credit scoring, neural simulation, a fuzzy model description, fuzzy inference rules, and a fuzzy Pospelov scale.
The research results are as follows: the authors have developed an unconventional algorithm for diagnosing corporate bankruptcy stages. This algorithm is based on the application of a system-wide law relating to decreases in integrated system entropy, contrasted with the sum of entropies of the relevant collated subsystems. This algorithm has been tested on a series of experimental observations of 30 agricultural enterprises in the Sterlitamak District of the Republic of Bashkortostan. We have thusly assessed the financial condition of borrowing companies, while controlling for the probability of a wide range of indicators.
Using this algorithm, the authors decided not to apply the rigorous requirements of the classical ‘least squares’ method used in regression analyses. A switch to a neural simulation approach in this algorithm necessitated an evaluation of the adequacy of the obtained model on the basis of a Bayesian approach. On the basis of this research, the authors propose that a regularisation of bankruptcy models has been achieved.
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Zack, Yuriy. "Cluster analysis for multidimensional objects in fuzzy data conditions." System research and information technologies, no. 2 (September 14, 2021): 18–34. http://dx.doi.org/10.20535/srit.2308-8893.2021.2.02.
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This article presents many different areas of practical applications of multivariate cluster analysis under conditions of fuzzy initial data that are described in the literature. New algorithms and formula expressions are proposed for combining various multi-dimensional objects, the parameters of which are given by fuzzy-sets, into clusters along with calculating the coordinates of the centroids of their membership functions. Various types of clustering criteria are formulated in the form of minimizing the weighted average and the sum of distances between the centroids of objects and clusters presented in different metrics, as well as maximizing the distances between the centroids of different clusters. The formulations and mathematical models of three different NP-hard problems of multidimensional clustering in fuzzy-data conditions are proposed; while solving them any of the considered optimality criteria can be used. Heuristic algorithms for the approximate solution of two formulated problems have been developed. The algorithm for solving the 1st problem is illustrated with a numerical example. The obtained results can serve as a direction for further research and have wide practical applications.
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Huang, Hong Zhong, Yong Hua Li, and L. H. Xue. "A Comprehensive Evaluation Model for Assessments of Grinding Machining Quality." Key Engineering Materials 291-292 (August 2005): 157–62. http://dx.doi.org/10.4028/www.scientific.net/kem.291-292.157.
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Grinding machining quality contains machining precision and grinding surface integrality. The factors influencing grinding machining quality have fuzzy characteristics. For example, the magnitude and measure methods of grinding surface roughness have fuzzy uncertainties. The grades of the machining quality are vague, and there is no definite boundary between “good” and “bad” machining quality. Analytical Hierarchy Process (AHP) combined with the fuzzy comprehensive evaluation technique is used to evaluate the grade condition of the quality in this paper. Considering the fuzziness of the factors, a two-stage fuzzy comprehensive evaluation model is proposed to evaluate the grinding machining quality. In light of different reliable degrees of different kinds of fuzzy operator models, the weighted average method is used. The membership degrees of the evaluation factors are determined by experts’ knowledge and experiences. The factor weights are calculated via the AHP method. Certain synthetic importance of each stage evaluation is presented through weighted sum of the relative important degree. Examples of conventional external grinding machining illustrate the effectiveness of the proposed model.
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Kurniawan, Muchamad, Rani Rotul Muhima, and Siti Agustini. "Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset." International Journal of Artificial Intelligence & Robotics (IJAIR) 2, no. 2 (December 1, 2020): 34. http://dx.doi.org/10.25139/ijair.v2i2.3030.
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One of the causes of forest fires is the lack of speed of handling when a fire occurs. This can be anticipated by determining how many extinguishing units are in the center of the hot spot. To get hotspots, NASA has provided an active fire dataset. The clustering method is used to get the most optimal centroid point. The clustering methods we use are K-Means, Fuzzy C-Means (FCM), and Average Linkage. The reason for using K-means is a simple method and has been applied in various areas. FCM is a partition-based clustering algorithm which is a development of the K-means method. The hierarchical based clustering method is represented by the Average Linkage method. The measurement technique that uses is the sum of the internal distance of each cluster. Elbow evaluation is used to evaluate the optimal cluster. The results obtained after conducting the K-Means trial obtained the best results with a total distance of 145.35 km, and the best clusters from this method were 4 clusters. Meanwhile, the total distance values obtained from the FCM and Linkage methods were 154.13 km and 266.61 km.
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Liang, Xifeng, Ming Peng, Jie Lu, and Chao Qin. "A Visual Servo Control Method for Tomato Cluster-Picking Manipulators Based on a T-S Fuzzy Neural Network." Transactions of the ASABE 64, no. 2 (2021): 529–43. http://dx.doi.org/10.13031/trans.13485.
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HighlightsA T-S fuzzy neural network was applied to the visual servo control system of a tomato picking manipulator.The T-S fuzzy neural network structure was designed, and collected data were used to train the neural network model.A visual servo control system for the picking manipulator based on the neural network was designed and tested.The T-S fuzzy neural network was superior to a BP neural network in visual servo control of the picking manipulator.Abstract. To reduce the computational load of image Jacobian matrix estimation and to avoid the appearance of singularity of a Jacobian matrix in the visual servo control of a picking manipulator, a T-S fuzzy neural network algorithm is proposed to replace the image Jacobian matrix. This better fits the hand-eye relationship by combining the knowledge structure of fuzzy reasoning with the self-learning ability of a neural network. The T-S fuzzy neural network was trained and tested by collecting the variation data of image features and joint angles; after training, the T-S fuzzy neural network was used to predict the joint angles of the picking manipulator. Simulation results show that the square sum of training errors and testing errors were 0.017 and 0.032, respectively, after training the T-S fuzzy neural network. A T-S fuzzy neural network controller was applied to the visual servo system of the picking robot, and the test results show that the average difference between the end-effector and the ultimate target location of the visual servo system based on the T-S fuzzy neural network controller was 0.0037 m, which was 79.44% less than that of the visual servo system based on a BP neural network. The final average error of image features was between 0.52 and 3.25 pixels, which was 74.932% less than that of the visual servo system based on the BP neural network. Keywords: Picking manipulator, Tomato clusters, T-S fuzzy neural network, Visual servoing.
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Makarova, I. V., E. M. Mukhametdinov, V. D. Shepelev, and Z. V. Al’metova. "Driving quality assessment system as part of intelligent onboard system." Herald of the Ural State University of Railway Transport, no. 1 (2020): 11–23. http://dx.doi.org/10.20291/2079-0392-2020-1-11-23.
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Driving parameters are controlled by external characteristics with the help of modern software tools; It can be, for example, the number of accelerations and deceleration, the speed of the vehicle, etc. However, the data obtained do not fully reflect the real situation. We propose a method of driving quality assessment by developing a neuro-fuzzy model of engine fuel consumption. In the first layer of the developed neuro-fuzzy model, the membership degrees of input parameters in fuzzy set are calculated. The outputs of the first layer neurons are the membership degrees of input values in fuzzy sets associated with the neurons. The second layer determines the degree to which the values of the input signals correspond to the rule conditions. The signals at the output of the third layer are the sum of products of weights and normalized degrees of the rules activity. It has been found that the cut of the surface appears smooth, which demonstrates that it is possible to obtain a control effect for any values of input variables from a given range. Values of the actual fuel consumption and fuel consumption according to the model are obtained. The relative error of individual measurements is calculated, as well as the average value of the model error. The maximum error was 3,5375 %, and the average model error was 0,4429 %. The developed method has been tested during road tests. The results of the tests confirm its adequacy on the public road (Ufa - Moscow route).
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Sidik, Galih Kurniawan, and Taufik Djatna. "A FAIRNESS MODEL BASED ON INTERVAL TYPE-2 FUZZY SET FOR ISLAMIC FINANCING SCORING IN INDONESIA." Airlangga International Journal of Islamic Economics and Finance 1, no. 1 (November 12, 2018): 43. http://dx.doi.org/10.20473/aijief.v1i1.10431.
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Conventional credit scoring model could lead to serious and unfair problems because in certain case it would incriminate one party in financing. Islamic financing scoring model complies with Sharia rules and ensures fairness among parties. Currently, there are no certain rules on Islamic financing scoring model which lead to subjective judgments. In the subjective judgments, words could mean different things to different people. Thus, this paper proposed and deployed models for scoring of default risk level by using Interval Type-2 Fuzzy Set model to support the subjective judgments in maintaining Sharia rules. Installment amount and the sum of delay period has used as variables for that scoring. Interval Type-2 Fuzzy Set model was proposed to support the subjective judgments in maintaining Sharia rules. Beginning delay period also used as a weight to the risk scoring results. Besides that, this paper also proposed the method for computing real loss value. It has used as a basis for fines computation according to default risk level, bad debt expense, and installment weighted average.
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Zhang, Yanpeng, Hua Qu, Weipeng Wang, and Jihong Zhao. "A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering." Mathematical Problems in Engineering 2020 (January 13, 2020): 1–17. http://dx.doi.org/10.1155/2020/9546792.
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Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.
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Akram, Muhammad, Maria Shabir, Ahmad N. Al-Kenani, and José Carlos R. Alcantud. "Hybrid Decision-Making Frameworks under Complex Spherical Fuzzy N -Soft Sets." Journal of Mathematics 2021 (March 23, 2021): 1–46. http://dx.doi.org/10.1155/2021/5563215.
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This paper presents the novel concept of complex spherical fuzzy N -soft set ( C S F N S f S ) which is capable of handling two-dimensional vague information with parameterized ranking systems. First, we propose the basic notions for a theoretical development of C S F N S f S s , including ranking functions, comparison rule, and fundamental operations (complement, union, intersection, sum, and product). Furthermore, we look into some properties of C S F N S f S s . We then produce three algorithms for multiattribute decision-making that take advantage of these elements. We demonstrate their applicability with the assistance of a numerical problem (selection of best third-party app of the year). A comparison with the performance of Pythagorean N -soft sets speaks for the superiority of our approach. Moreover, with an aim to expand the range of techniques for multiattribute group decision-making problems, we design a C S F N S f -TOPSIS method. We use a complex spherical fuzzy N -soft weighted average operator in order to aggregate the decisions of all experts according to the power of the attributes and features of alternatives. We present normalized-Euclidean distances (from the alternatives to both the C S F N S f positive and negative ideal solutions, respectively) and revised closeness index in order to produce a best feasible alternative. As an illustration, we design a mathematical model for the selection of the best physiotherapist doctor of Mayo hospital, Lahore. We conduct a comparison with the existing complex spherical fuzzy TOPSIS method that confirms the stability of the proposed model and the reliability of its results.
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KRISHNAN, M. MUTHU RAMA, S. VINITHA SREE, DHANJOO N. GHISTA, EDDIE Y. K. NG, SWAPNA, ALVIN P. C. ANG, KWAN-HOONG NG, and JASJIT S. SURI. "AUTOMATED DIAGNOSIS OF CARDIAC HEALTH USING RECURRENCE QUANTIFICATION ANALYSIS." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240014. http://dx.doi.org/10.1142/s0219519412400143.
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The sum total of millions of cardiac cell depolarization potentials can be represented using an electrocardiogram (ECG). By inspecting the P-QRS-T wave in the ECG of a patient, the cardiac health can be diagnosed. Since the amplitude and duration of the ECG signal are too small, subtle changes in the ECG signal are very difficult to be deciphered. In this work, the heart rate variability (HRV) signal has been used as the base signal to observe the functioning of the heart. The HRV signal is non-linear and non-stationary. Recurrence quantification analysis (RQA) has been used to extract the important features from the heart rate signals. These features were fed to the fuzzy, Gaussian mixture model (GMM), and probabilistic neural network (PNN) classifiers for automated classification of cardiac bio-electrical contractile disorders. Receiver operating characteristics (ROC) was used to test the performance of the classifiers. In our work, the Fuzzy classifier performed better than the other classifiers and demonstrated an average classification accuracy, sensitivity, specificity, and positive predictive value of more than 83%. The developed system is suitable to evaluate large datasets.
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Wang, Xin, and Bo Luo. "Application of Service Modular Design Based on a Fuzzy Design Structure Matrix: A Case Study from the Mining Industry." Mathematical Problems in Engineering 2021 (June 15, 2021): 1–19. http://dx.doi.org/10.1155/2021/5067092.
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The development of customized service is an important way to transform and upgrade China’s mining industry. However, in practice, there remain problems, such as the slow market response speed of service providers and the contradiction between the large-scale development of service providers and the personalized service needs of service demanders. This paper uses the theory and method of service modular design to solve these problems and explores the process-based service modular design method. Service modular design depends largely on the determination of the relationship between service activities and the reasonable division of modules. However, previous research has rarely made use of modular design methods and modeling tools in the mining service context. At the same time, evaluations of the relationship between service activities relying on knowledge and those relying on experience have been inconclusive. Therefore, this paper proposes a service modularization design method based on the fuzzy relation analysis of a design structure matrix (DSM) that solves the optimal module partition scheme. Triangular fuzzy number and fuzzy evidence theory are used to evaluate and fuse the multidimensional and heterogeneous relationship between service activities, and the quantitative processing of the comprehensive relationship between service activities is carried out. On this basis, the service module structure is divided, followed by the construction of the mathematical programming model with the maximum sum of the average cohesion degree in the module and the average coupling degree between modules as the driving goal. The genetic algorithm is used to solve the problem, and the optimal module division result is obtained. Finally, taking the service modular design of SHD coal production enterprises in China as an example, the feasibility of the proposed method is verified.
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Bhushan, Shashi, Manoj Kumar, Pramod Kumar, Thompson Stephan, Achyut Shankar, and Peide Liu. "FAJIT: a fuzzy-based data aggregation technique for energy efficiency in wireless sensor network." Complex & Intelligent Systems 7, no. 2 (January 8, 2021): 997–1007. http://dx.doi.org/10.1007/s40747-020-00258-w.
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AbstractWireless sensor network (WSN) is used to sense the environment, collect the data, and further transmit it to the base station (BS) for analysis. A synchronized tree-based approach is an efficient approach to aggregate data from various sensor nodes in a WSN environment. However, achieving energy efficiency in such a tree formation is challenging. In this research work, an algorithm named fuzzy attribute-based joint integrated scheduling and tree formation (FAJIT) technique for tree formation and parent node selection using fuzzy logic in a heterogeneous network is proposed. FAJIT mainly focuses on addressing the parent node selection problem in the heterogeneous network for aggregating different types of data packets to improve energy efficiency. The selection of parent nodes is performed based on the candidate nodes with the minimum number of dynamic neighbors. Fuzzy logic is applied in the case of an equal number of dynamic neighbors. In the proposed technique, fuzzy logic is first applied to WSN, and then min–max normalization is used to retrieve normalized weights (membership values) for the given edges of the graph. This membership value is used to denote the degree to which an element belongs to a set. Therefore, the node with the minimum sum of all weights is considered as the parent node. The result of FAJIT is compared with the distributed algorithm for Integrated tree Construction and data Aggregation (DICA) on various parameters: average schedule length, energy consumption data interval, the total number of transmission slots, control overhead, and energy consumption in the control phase. The results demonstrate that the proposed algorithm is better in terms of energy efficiency.
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Subudhi, Asit, Subhransu S. Jena, and Sukanta Sabut. "Automated Detection of Brain Stroke in MRI with Hybrid Fuzzy C-Means Clustering and Random Forest Classifier." International Journal of Computational Intelligence and Applications 18, no. 03 (September 2019): 1950018. http://dx.doi.org/10.1142/s1469026819500184.
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Neuroimaging investigation is an essential parameter to detect infarct lesion in stroke patients. Precise detection of brain lesions is an important task related to impaired behavior. In this paper, we aimed to develop an automatic method to segment and classify infarct lesion in diffusion-weighted imaging (DWI) of brain MRI. The method includes hybrid fuzzy [Formula: see text]-means (HFCM) clustering in which the structure of [Formula: see text]-means clustering is modified with rough sets and fuzzy sets to improve the segmentation performance with self-adjusted intensity thresholds. Quantitative evaluation was carried out on 128 MRI slices of brain image collected from ischemic stroke patients at the Department of Radiology, IMS and SUM Hospital, Bhubaneswar. The informative statistical features have been extracted using gray-level co-occurrence matrix (GLCM) and used to classify the types of stroke infarct according to the Oxfordshire Community Stroke Project (OCSP) classification. The parameters such as accuracy, Dice similarity index (DSI) and Jaccard index (JI) were utilized to evaluate the effectiveness of the proposed method in detecting the stroke lesions. The segmentation method achieved the average accuracy, DSI and JI of 96.8%, 95.8% and 92.2%, respectively, in support vector machine (SVM) classifier. The obtained results are higher in terms of random forest (RF) classification. With a high Dice coefficient of 0.958 and other evaluated parameters, the proposed method outperforms earlier published results.
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Agbodah, Kobina, and Adjei Peter Darko. "Probabilistic Linguistic Aggregation Operators Based on Einstein t-Norm and t-Conorm and Their Application in Multi-Criteria Group Decision Making." Symmetry 11, no. 1 (January 2, 2019): 39. http://dx.doi.org/10.3390/sym11010039.
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One of the major problems of varied knowledge-based systems has to do with aggregation and fusion. Pang’s probabilistic linguistic term sets denotes aggregation of fuzzy information and it has attracted tremendous interest from researchers recently. The purpose of this article is to deal investigating methods of information aggregation under the probabilistic linguistic environment. In this situation we defined certain Einstein operational laws on probabilistic linguistic term elements (PLTESs) based on Einstein product and Einstein sum. Consequently, we develop some probabilistic linguistic aggregation operators, notably the probabilistic linguistic Einstein average (PLEA) operators, probabilistic linguistic Einstein geometric (PLEG) operators, weighted probabilistic linguistic Einstein average (WPLEA) operators, weighted probabilistic linguistic Einstein geometric (WPLEG) operators. These operators extend the weighted averaging operator and the weighted geometric operator for the purpose of aggregating probabilistic linguistic terms values respectively. Einstein t-norm and Einstein t-conorm constitute effective aggregation tools and they allow input arguments to reinforce each other downwardly and upwardly respectively. We then generate various properties of these operators. With the aid of the WPLEA and WPLEG, we originate the approaches for the application of multiple attribute group decision making (MAGDM) with the probabilistic linguistic term sets (PLTSs). Lastly, we apply an illustrative example to elucidate our proposed methods and also validate their potentials.
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Ge, Luzhen, Zhilun Yang, Zhe Sun, Gan Zhang, Ming Zhang, Kaifei Zhang, Chunlong Zhang, Yuzhi Tan, and Wei Li. "A Method for Broccoli Seedling Recognition in Natural Environment Based on Binocular Stereo Vision and Gaussian Mixture Model." Sensors 19, no. 5 (March 6, 2019): 1132. http://dx.doi.org/10.3390/s19051132.
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Illumination in the natural environment is uncontrollable, and the field background is complex and changeable which all leads to the poor quality of broccoli seedling images. The colors of weeds and broccoli seedlings are close, especially under weedy conditions. The factors above have a large influence on the stability, velocity and accuracy of broccoli seedling recognition based on traditional 2D image processing technologies. The broccoli seedlings are higher than the soil background and weeds in height due to the growth advantage of transplanted crops. A method of broccoli seedling recognition in natural environments based on Binocular Stereo Vision and a Gaussian Mixture Model is proposed in this paper. Firstly, binocular images of broccoli seedlings were obtained by an integrated, portable and low-cost binocular camera. Then left and right images were rectified, and a disparity map of the rectified images was obtained by the Semi-Global Matching (SGM) algorithm. The original 3D dense point cloud was reconstructed using the disparity map and left camera internal parameters. To reduce the operation time, a non-uniform grid sample method was used for the sparse point cloud. After that, the Gaussian Mixture Model (GMM) cluster was exploited and the broccoli seedling points were recognized from the sparse point cloud. An outlier filtering algorithm based on k-nearest neighbors (KNN) was applied to remove the discrete points along with the recognized broccoli seedling points. Finally, an ideal point cloud of broccoli seedlings can be obtained, and the broccoli seedlings recognized. The experimental results show that the Semi-Global Matching (SGM) algorithm can meet the matching requirements of broccoli images in the natural environment, and the average operation time of SGM is 138 ms. The SGM algorithm is superior to the Sum of Absolute Differences (SAD) algorithm and Sum of Squared Differences (SSD) algorithms. The recognition results of Gaussian Mixture Model (GMM) outperforms K-means and Fuzzy c-means with the average running time of 51 ms. To process a pair of images with the resolution of 640×480, the total running time of the proposed method is 578 ms, and the correct recognition rate is 97.98% of 247 pairs of images. The average value of sensitivity is 85.91%. The average percentage of the theoretical envelope box volume to the measured envelope box volume is 95.66%. The method can provide a low-cost, real-time and high-accuracy solution for crop recognition in natural environment.
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Alhumade, Hesham, Hegazy Rezk, Abdulrahim A. Al-Zahrani, Sharif F. Zaman, and Ahmed Askalany. "Artificial Intelligence Based Modelling of Adsorption Water Desalination System." Mathematics 9, no. 14 (July 16, 2021): 1674. http://dx.doi.org/10.3390/math9141674.
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The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.
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Rasheduzzaman, Md, Md Amirul Islam, and Rashedur M. Rahman. "Workload Prediction on Google Cluster Trace." International Journal of Grid and High Performance Computing 6, no. 3 (July 2014): 34–52. http://dx.doi.org/10.4018/ijghpc.2014070103.
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Workload prediction in cloud systems is an important task to ensure maximum resource utilization. So, a cloud system requires efficient resource allocation to minimize the resource cost while maximizing the profit. One optimal strategy for efficient resource utilization is to timely allocate resources according to the need of applications. The important precondition of this strategy is obtaining future workload information in advance. The main focus of this analysis is to design and compare different forecasting models to predict future workload. This paper develops model through Adaptive Neuro Fuzzy Inference System (ANFIS), Non-linear Autoregressive Network with Exogenous inputs (NARX), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). Public trace data (workload trace version II) which is made available by Google were used to verify the accuracy, stability and adaptability of different models. Finally, this paper compares these prediction models to find out the model which ensures better prediction. Performance of forecasting techniques is measured by some popular statistical metric, i.e., Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Sum of Squared Error (SSE), Normalized Mean Squared Error (NMSE). The experimental result indicates that NARX model outperforms other models, e.g., ANFIS, ARIMA, and SVR.
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Huang, Fada, Yifang Tang, Liang Huang, and Haitao Zhang. "Application Of Comprehensive Evaluation Method In Oil Well Classification." E3S Web of Conferences 131 (2019): 01061. http://dx.doi.org/10.1051/e3sconf/201913101061.
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China’s oil resources are very rich, but the effect of oil exploitation is not good. Precise prediction of oil production plays an extremely important role in formulating a reasonable oil planning plan and determining the direction of investment. In order to improve the accuracy of oil well exploitation, it is necessary to establish a scientific oil production forecasting system. First, pre-treatment research on the data of Baikouquan Oilfield to ensure the quality of oilfield data. Secondly, the main controlling factors affecting the production of oil wells are analyzed, and the single evaluation is carried out by the coefficient of variation method, the entropy method and the mean square error method. Then, the pre-examination test of the consistency test is performed on the single evaluation result, and the combination of the evaluation methods is selected. The combined evaluation method is the average method, and the fuzzy Borda method. It is again tested whether the combined evaluation result has passed the consistency test. After the consistency test, the sum of the squares of the errors of the three combined evaluations is calculated, and the optimal one is optimized. The combined evaluation method was used for the second combination evaluation, and the results of all the evaluation methods were compared to observe whether the combined evaluation results were effective, and 8 main control factors were analyzed and screened. Finally, the main control factor data analyzed by comprehensive evaluation is used for discriminant analysis, and the fitting effect is compared. It is found that the fitting results are basically consistent with the data of the oilfield site, indicating that the results of the main control factors analyzed by the comprehensive evaluation are better. The prediction accuracy meets the engineering requirements and is of great significance for the prediction of on-site oilfield production.
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Othman, Arsalan Ahmed, Ahmed F. Al-Maamar, Diary Ali Mohammed Amin Al-Manmi, Veraldo Liesenberg, Syed E. Hasan, Ahmed K. Obaid, and Ayad M. Fadhil Al-Quraishi. "GIS-Based Modeling for Selection of Dam Sites in the Kurdistan Region, Iraq." ISPRS International Journal of Geo-Information 9, no. 4 (April 15, 2020): 244. http://dx.doi.org/10.3390/ijgi9040244.
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Iraq, a country in the Middle East, has suffered severe drought events in the past two decades due to a significant decrease in annual precipitation. Water storage by building dams can mitigate drought impacts and assure water supply. This study was designed to identify suitable sites to build new dams within the Al-Khabur River Basin (KhRB). Both the fuzzy analytic hierarchy process (AHP) and the weighted sum method (WSM) were used and compared to select suitable dam sites. A total of 14 layers were used as input dataset (i.e., lithology, tectonic zones, distance to active faults, distance to lineaments, soil type, land cover, hypsometry, slope gradient, average precipitation, stream width, Curve Number Grid, distance to major roads, distance to towns and cities, and distance to villages). Landsat-8/Operational Land Imager (OLI) and QuickBird optical images were used in the study. Three types of accuracies were tested: overall, suitable pixels by number, and suitable pixels by weight. Based on these criteria, we determined that 11 sites are suitable for locating dams for runoff harvesting. Results were compared to the location of 21 preselected dams proposed by the Ministry of Agricultural and Water Resources (MAWR). Three of these dam sites coincide with those proposed by the MAWR. The overall accuracies of the 11 dams ranged between 76.2% and 91.8%. The two most suitable dam sites are located in the center of the study area, with favorable geology, adequate storage capacity, and in close proximity to the population centers. Of the two selection methods, the AHP method performed better as its overall accuracy is greater than that of the WSM. We argue that when stream discharge data are not available, use of high spatial resolution QuickBird imageries to determine stream width for discharge estimation is acceptable and can be used for preliminary dam site selection. The study offers a valuable and relatively inexpensive tool to decision-makers for eliminating sites having severe limitations (less suitable sites) and focusing on those with the least restriction (more suitable sites) for dam construction.
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Zubenko, D., S. Zakurdai, and O. Donets. "USE OF NEURAL NETWORKS TO MAKE COMPLEX DECISIONS TO OPERATE ELECTRIC TRANSPORT UNDER UNCERTAINTY." Municipal economy of cities 6, no. 159 (November 27, 2020): 173–75. http://dx.doi.org/10.33042/2522-1809-2020-6-159-173-175.
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The use of neural networks to solve the problems of insolubility and the solution of complex computational equations becomes a common practice in academic circles and industry. It has been shown that, despite the complexity, these problems can be formulated as a set of equations, and the key is to find zeros from them. Zero Neural Networks (ZNNs), as a class of neural networks specially designed to find zeros of equations, have played an indispensable role in online decision-changing problems over time in recent years, and many fruitful research results have been documented in literature. The purpose of this article is to provide a comprehensive overview of ZNN studies, including ZNN continuous time and discrete time models for solving various problems, and their application in motion planning and superfluous manipulator management, chaotic system tracking, or even population control in mathematical biological sciences. Considering the fact that real-time performance is in demand for time-varying problems in practice, analysis of the stability and convergence of various ZNN models with continuous time is considered in a unified form in detail. In the case of solving the problems of discrete time, procedures are summarized for how to discriminate a continuous ZNN model and methods for obtaining an accuracy decision. Approaches based on the neural network to address various nodal tasks have attracted considerable attention in many areas. For example, an adaptive fuzzy controller based on a neural network is constructed for a class of nonlinear systems with discrete time with a dead zone with discrete time in. An applied decentralized circuit, based on a neural network, is presented for multiple nonlinear input and multiple output systems (MIMO) using the methods of the reverse step in. Such a scheme guarantees a uniform limiting limit of all signals in a closed system relative to the average square. In order to overcome the structural complexity of the nonlinear feedback structure, uses the method of dividing variables for the decomposition of unknown functions of all state variables into the sum of smooth functions of each dynamic error.
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Salimov Vagif Hasan Oglu. "APPLICATION OF FUZZY ELECTRE METHOD WITH TRAPEZOIDAL FUZZY NUMBERS." World Science, no. 8(69) (August 10, 2021). http://dx.doi.org/10.31435/rsglobal_ws/30082021/7654.
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The article is devoted to the problem of multi-criteria decision-making. Methods for solving this problem can be divided into two large groups:methods using the aggregation of all alternatives according to all criteria and the solution of the resulting single-criterion problem. The second group isassociated with the procedure of pairwise comparisons and stepwise aggregation. The first group includes methods: weighted average sum,product and their various modifications, the second group includes - AHP, ELECTRE, TOPSIS, PROMETHEE, ELECTRE. For many problemsassessment of the criteria implemented by experts and presented in linguistic form. The effective approach for dealing with linguistic information is fuzzyset theory proposed by L. Zadeh. In this paper is proposed fuzzy ELECTRE method. This method is presented in details. As application problem is usedthe equipment selection problem The issues of practical implementation of this method are discussed in details. The results of the solution test problem at all stages are presented.
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Al-Refaie, Abbas, Ghaleb Abbasi, and Dina Ghanim. "Proposed α-cut CUSUM and EWMA Control Charts for Fuzzy Response Observations." International Journal of Reliability, Quality and Safety Engineering, September 25, 2020, 2150012. http://dx.doi.org/10.1142/s0218539321500121.
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This research proposesalpha ([Formula: see text]-cut Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) control charts with fuzzy response observations in a manufacturing process under the existence of mean shift utilizing the fuzzy logic. In this research, the replicate’s observation is a fuzzy number represented by a triangular membership function, with the lower, average, and upper observation values. The fuzzy numbers are then normalized and assigned as input to the fuzzy logic, while a common output measure (COM) value is the output. Finally, the original values of the COM values are employed in developing the EWMA and CUSUM control charts with different [Formula: see text]-cut values. Three real case studies are adopted to illustrate the proposed EWMA and CUSUM control charts; including piston inside diameter, cap’s angel, and tablet weight. Results showed that the proposed EWMA and CUSUM control charts efficiently monitor fuzzy observations and detect the shift in process means. Moreover, the amount mean shift and [Formula: see text]-cut values affect the decision on process condition. In conclusion, the proposed approach is found effective in monitoring quality characteristic of fuzzy observations under mean shift which can be applied in a wide range of business applications.
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"Multi-Objective Restricted Solid Transportation Problem in Intuitionistic Fuzzy Environment with Emission Cost." International Journal of Recent Technology and Engineering 8, no. 2S3 (August 10, 2019): 722–27. http://dx.doi.org/10.35940/ijrte.b1134.82s319.
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Transportation plays key role in logistic and supply chain management for decreasing cost and enhances service. The transport sector contributes 23% of the total CO2 emissions in the world according to the latest estimates of the International Energy Agency (IEA).There is a direct link between weight of the quantity transported and co2 emission for the freight transport. This paper presents multi objective restricted solid transportation problem in intuitionistic fuzzy ambiance with emission cost which is based on weight of the quantity transported and vehicle cost under some restriction on transported amount. An extra constraint on the total budget at each destination is imposed. Transportation models are formulated under crisp and fuzzy environments and fuzzy models are converted into crisp using average method. The total time and emission cost based on weight of the quantity transported for restricted and unrestricted models are compared. The optimal solution is obtained by using weighted sum method and Lingo 13.0 Software. Mathematical example is given to validate the proposed mode
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"Development of Line Follower Robot with Camera Surveillance System." International Journal of Recent Technology and Engineering 8, no. 4 (November 30, 2019): 2192–97. http://dx.doi.org/10.35940/ijrte.d7861.118419.
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This study describes the development of a line follower robot for a surveillance camera monitoring system. An effective closed loop control fuzzy logic algorithm is used to constantly correct wrong movements of the mobile robot using a feedback mechanism. The robot senses a black line on a white surface and endeavors itself accordingly to follow the track. A manual navigation system has also been designed to overrule the automatic navigation control of the robot to reposition itself back on the track whenever it strays from the path unintentionally. The fuzzy controller algorithm is an advanced method to ensure the line follower robot moves accurately on the track. It is a replacement control technique of traditional switching method. To fuzzifying the digital input data of four infrared sensor that detecting the line, the data is converted into error and delta error that represent the current and previous position of the robot relative to the line that it follows. There are nine base rules that have been created with two inputs which are error and delta error to the robot direction whether to go to the right, move forward or to the left. Then, for defuzzification, center of sum and centroid of area method have been used to calculate the defuzzied value using trapezium area formulae. Based on the comparison between both control techniques, it is found that the line following surveillance robot with fuzzy logic controller works faster than conventional switching method to complete the same task with the average oscillation length using the fuzzy logic controller is reduced to half.
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Bhushan, Shashi, Manoj Kumar, Pramod Kumar, Thompson Stephan, Achyut Shankar, and Peide Liu. "FAJIT: a fuzzy-based data aggregation technique for energy efficiency in wireless sensor network." Complex & Intelligent Systems, January 8, 2021. http://dx.doi.org/10.1007/s40747-020-00258-w.
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AbstractWireless sensor network (WSN) is used to sense the environment, collect the data, and further transmit it to the base station (BS) for analysis. A synchronized tree-based approach is an efficient approach to aggregate data from various sensor nodes in a WSN environment. However, achieving energy efficiency in such a tree formation is challenging. In this research work, an algorithm named fuzzy attribute-based joint integrated scheduling and tree formation (FAJIT) technique for tree formation and parent node selection using fuzzy logic in a heterogeneous network is proposed. FAJIT mainly focuses on addressing the parent node selection problem in the heterogeneous network for aggregating different types of data packets to improve energy efficiency. The selection of parent nodes is performed based on the candidate nodes with the minimum number of dynamic neighbors. Fuzzy logic is applied in the case of an equal number of dynamic neighbors. In the proposed technique, fuzzy logic is first applied to WSN, and then min–max normalization is used to retrieve normalized weights (membership values) for the given edges of the graph. This membership value is used to denote the degree to which an element belongs to a set. Therefore, the node with the minimum sum of all weights is considered as the parent node. The result of FAJIT is compared with the distributed algorithm for Integrated tree Construction and data Aggregation (DICA) on various parameters: average schedule length, energy consumption data interval, the total number of transmission slots, control overhead, and energy consumption in the control phase. The results demonstrate that the proposed algorithm is better in terms of energy efficiency.
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Eghbali-Zarch, Maryam, Reza Tavakkoli-Moghaddam, Kazem Dehghan-Sanej, and Amin Kaboli. "Prioritizing the effective strategies for construction and demolition waste management using fuzzy IDOCRIW and WASPAS methods." Engineering, Construction and Architectural Management ahead-of-print, ahead-of-print (April 13, 2021). http://dx.doi.org/10.1108/ecam-08-2020-0617.
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PurposeThe construction industry is a key driver of economic growth. However, the adverse impacts of construction and demolition waste (CDW) resulted from the active construction projects on the economy, environment, public health and social life necessitates an appropriate control and management of this waste stream. Developing and promoting the construction and demolition waste management (CDWM) hierarchy program at the strategic level is essential.Design/methodology/approachThis study aims to propose a hybrid decision model that hybridizes the Integrated Determination of Objective Criteria Weights (IDOCRIW) and weighted aggregated sum product assessment (WASPAS) under a fuzzy environment.FindingsThe proposed method ranks the potential strategic alternatives by the sustainable development criteria to improve the performance of CDWM. As indicated in the results, the fuzzy approach in the decision-making process enables the transformation of linguistic variables into fuzzy numbers that show uncertainty and ambiguity in real-world systems. Moreover, the close correlation between the final ranking of the proposed methodology and the average priority order of the strategic alternatives obtained by five different multi-criteria decision-making (MCDM) methods implies the validity of the model performance.Practical implicationsThis proposed model is an appropriate tool to effectively decide on the development of CDWM from a strategic point of view. It aims to establish an MCDM framework for the evaluation of effective strategies for CDWM according to the indices of sustainable development. Implementing proper operational plans and conducting research in CDWM has the highest priority, and enacting new and more stringent laws, rules and regulations against the production of CDW has secondary priority. This study contributes to the field by optimizing the CDWM by applying the top-priority strategies resulted from the proposed fuzzy hybrid MCDM methodology by the decision-makers or policy-makers to reach the best managerial strategic plan.Originality/valueIn the proposed methodology, the IDOCRIW technique is utilized and updated with the triangular fuzzy numbers for the first time in the literature to derive the weights of sustainable development criteria. The fuzzy WASPAS method is utilized for evaluation and providing a final ranking of the strategic alternatives.
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Xu, Yajin, Qiong Luo, and Hong Shu. "Optimal Excess Commuting Evaluation Based on Local Minimal Costs." Transportation Research Record: Journal of the Transportation Research Board, August 27, 2021, 036119812110315. http://dx.doi.org/10.1177/03611981211031530.
Full textAbstract:
Excess commuting refers to the value of unnecessary commuting or distance costs. Traditional commuting distance models adapt the most efficient scenario with people working in the nearest workplace geographically. Even though there have been some attempts to include constraints with commuter attributes and neighborhood features, problems arise with traditional geographical space and the subjectivity of these predefined characteristics. In this paper, we propose a method to calculate theoretical local minimal costs, which considers preferences that are inherently behavioral based on current work–home trips in the process of reassigning the work–home configuration. Our method is based on a feature space with a higher dimension and with the enlargement of attributes and relations of and between commuters and neighborhoods. Additionally, our solution is arrived at innovatively by improved Fuzzy C-Means clustering and linear programming. Unlike traditional clustering algorithms, our improved method adapts entropy information and selects the initial parameters based on the actual data rather than on prior knowledge. Using the real origin–destination matrix, theoretical minimal costs are calculated within each cluster, referred to as local minimal costs, and the average sum of local minimal costs is our theoretical minimal cost. The difference between the expected minimal cost and the actual cost is the excess commuting. Using our method, experimental results show that only 13% of the daily commuting distance in Wuhan could be avoided, and the theoretical distance is approximately 1.06 km shorter than the actual commuting distance.