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  • Open Access

    ARTICLE

    Modified PSO Algorithm on Recurrent Fuzzy Neural Network for System Identification

    Chung Wen Hung, Wei Lung Mao, Han Yi Huang

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 329-341, 2019, DOI:10.31209/2019.100000093

    Abstract Nonlinear system modeling and identification is the one of the most important areas in engineering problem. The paper presents the recurrent fuzzy neural network (RFNN) trained by modified particle swarm optimization (MPSO) methods for identifying the dynamic systems and chaotic observation prediction. The proposed MPSO algorithms mainly modify the calculation formulas of inertia weights. Two MPSOs, namely linear decreasing particle swarm optimization (LDPSO) and adaptive particle swarm optimization (APSO) are developed to enhance the convergence behavior in learning process. The RFNN uses MPSO based method to tune the parameters of the membership functions, and it uses gradient descent (GD) based… More >

  • Open Access

    ARTICLE

    Predictive Control Algorithm for Urban Rail Train Brake Control System Based on T-S Fuzzy Model

    Xiaokan Wang1, 2, *, Qiong Wang2, Shuang Liang3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1859-1867, 2020, DOI:10.32604/cmc.2020.011032

    Abstract Urban rail transit has the advantages of large traffic capacity, high punctuality and zero congestion, and it plays an increasingly important role in modern urban life. Braking system is an important system of urban rail train, which directly affects the performance and safety of train operation and impacts passenger comfort. The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity. Also, urban rail transit has the characteristics of high speed, short station distance, frequent starting, and frequent braking. This makes the braking control system constitute a time-varying, time-delaying and nonlinear control… More >

  • Open Access

    ARTICLE

    Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

    Yifei Wei1, *, Yinxiang Qu1, Min Zhao1, Lianping Zhang2, F. Richard Yu3

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1515-1532, 2020, DOI:10.32604/cmc.2020.09130

    Abstract Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,… More >

  • Open Access

    ARTICLE

    Applying ANN, ANFIS and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

    Amin Bemani1, Alireza Baghban2, Shahaboddin Shamshirband3, 4, *, Amir Mosavi5, 6, 7, Peter Csiba7, Annamaria R. Varkonyi-Koczy5, 7

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1175-1204, 2020, DOI:10.32604/cmc.2020.07723

    Abstract In the present work, a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide. Four different machine learning algorithms of radial basis function, multi-layer perceptron (MLP), artificial neural networks (ANN), least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and the dissociation constant of acid. To evaluate the proposed models, different graphical and statistical analyses, along with novel sensitivity analysis, are carried out.… More >

  • Open Access

    ARTICLE

    Intuitionistic Fuzzy Petri Nets Model Based on Back Propagation Algorithm for Information Services

    Junhua Xi1, *, Kouquan Zheng1, Jianfeng Ma1, Jungang Yang1, Zhiyao Liang2

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 605-619, 2020, DOI:10.32604/cmc.2020.06343

    Abstract Intuitionistic fuzzy Petri net is an important class of Petri nets, which can be used to model the knowledge base system based on intuitionistic fuzzy production rules. In order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems, a new Petri net modeling method is proposed by introducing BP (Error Back Propagation) algorithm in neural networks. By judging whether the transition is ignited by continuous function, the intuitionistic fuzziness of classical BP algorithm is extended to the parameter learning and training, which makes Petri network have stronger generalization ability and adaptive function, and the reasoning result is… More >

  • Open Access

    ARTICLE

    A Comprehensive Model for Structural Non-Probabilistic Reliability and the Key Algorithms

    Wencai Sun1, ∗, Zichun Yang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 309-332, 2020, DOI:10.32604/cmes.2020.08386

    Abstract It is very difficult to know the exact boundaries of the variable domain for problems with small sample size, and the traditional convex set model is no longer applicable. In view of this, a novel reliability model was proposed on the basis of the fuzzy convex set (FCS) model. This new reliability model can account for different relations between the structural failure region and variable domain. Key computational algorithms were studied in detail. First, the optimization strategy for robust reliability is improved. Second, Monte Carlo algorithms (i.e., uniform sampling method) for hyper-ellipsoidal convex sets were studied in detail, and errors… More >

  • Open Access

    ARTICLE

    Synthesized AI LMI-based Criterion for Mechanical Systems

    Jcy Chen1,*, Wc Chen1, Tim Chen1, Alex Wilson2, N. Fadilah Jamaludin3, Nertrand Kapron1, Tim Chen4,5, John Burno5

    Sound & Vibration, Vol.53, No.6, pp. 245-250, 2019, DOI:10.32604/sv.2019.04233

    Abstract This paper proposes a novel artificial intelligence sythethized controller in the mechanical system which has high speed computation because of the LMI type criterion. The proposed membership functions are adopted and stabilization criterion of the closed-loop T-S fuzzy systems are obtained through a new parametrized LMI (linear matrix) inequality which is rearranged by machine learning membership functions. More >

  • Open Access

    ARTICLE

    Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

    Yun Wang1, Fazli Subhan2, Shahaboddin Shamshirband3, 4, *, Muhammad Zubair Asghar5, Ikram Ullah5, Ammara Habib5

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 631-655, 2020, DOI:10.32604/cmc.2020.07920

    Abstract The feedback collection and analysis has remained an important subject matter for long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction… More >

  • Open Access

    ARTICLE

    Solving Fully Fuzzy Nonlinear Eigenvalue Problems of Damped Spring-Mass Structural Systems Using Novel Fuzzy-Affine Approach

    S. Rout1, S. Chakraverty1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 947-980, 2019, DOI:10.32604/cmes.2019.08036

    Abstract The dynamic analysis of damped structural system by using finite element method leads to nonlinear eigenvalue problem (NEP) (particularly, quadratic eigenvalue problem). In general, the parameters of NEP are considered as exact values. But in actual practice because of different errors and incomplete information, the parameters may have uncertain or vague values and such uncertain values may be considered in terms of fuzzy numbers. This article proposes an efficient fuzzy-affine approach to solve fully fuzzy nonlinear eigenvalue problems (FNEPs) where involved parameters are fuzzy numbers viz. triangular and trapezoidal. Based on the parametric form, fuzzy numbers have been transformed into… More >

  • Open Access

    ARTICLE

    Identification of axillary buds of potato seedlings based on a vision system with fuzzy logic

    Martínez Corral L1, E Martínez-Rubin2, F F lores-García3, M Vázquez-Rueda3, J Frías-Ramírez2, MA Segura-Castruita2

    Phyton-International Journal of Experimental Botany, Vol.80, pp. 79-84, 2011, DOI:10.32604/phyton.2011.80.079

    Abstract Potato (Solanum tuberosum L.) is a crop whose production yield at national level is very low compared with that in the most productive countries. This is because it is a partially automated crop with deficient and inadequate agronomic practices, low technification levels and great quantity of work wages required per hectare of cultivation. The necessity to generate technical and modern procedures that increase crop production, quality and yield has fostered development of projects leading to obtain seedlings free of pathogens with material of high genetic, physiological and sanitary quality. Utilization of a vision system for the computerized visual recognition of… More >

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