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  • Open Access


    BDI Agent and QPSO-based Parameter Optimization for a Marine Generator Excitation Controller

    Wei Zhang1, Weifeng Shi2, Bing Sun3

    Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 423-431, 2019, DOI:10.31209/2018.100000045

    Abstract An intelligent optimization algorithm for a marine generator excitation controller is proposed to improve dynamic performance of shipboard power systems. This algorithm combines a belief–desire–intention agent with a quantum-behaved particle swarm optimization (QPSO) algorithm to optimize a marine generator excitation controller. The shipboard zonal power system is simulated under disturbance due to load change or severe fault. The results show that the proposed optimization algorithm can improve marine generator stability compared with conventional excitation controllers under various operating conditions. Moreover, the proposed intelligent algorithm is highly robust because its performance is insensitive to the accuracy More >

  • Open Access


    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 More >

  • Open Access


    Development of a Data‐Driven ANFIS Model by Using PSO‐LSE Method for Nonlinear System Identification

    Ching‐Yi Chen, Yi‐Jen Lin

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 319-327, 2019, DOI:10.31209/2019.100000093

    Abstract In this study, a systematic data-driven adaptive neuro-fuzzy inference system (ANFIS) modelling methodology is proposed. The new methodology employs an unsupervised competitive learning scheme to build an initial ANFIS structure from input-output data, and a high-performance PSO-LSE method is developed to improve the structure and to identify the consequent parameters of ANFIS model. This proposed modelling approach is evaluated using several nonlinear systems and is shown to outperform other modelling approaches. The experimental results demonstrate that our proposed approach is able to find the most suitable architecture with better results compared with other methods from More >

  • Open Access


    An Accelerated Convergent Particle Swarm Optimizer (ACPSO) of Multimodal Functions

    Yasir Mehmood, Waseem Shahzad

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 91-103, 2019, DOI:10.31209/2018.100000017

    Abstract Particle swarm optimization (PSO) algorithm is a global optimization technique that is used to find the optimal solution in multimodal problems. However, one of the limitation of PSO is its slow convergence rate along with a local trapping dilemma in complex multimodal problems. To address this issue, this paper provides an alternative technique known as ACPSO algorithm, which enables to adopt a new simplified velocity update rule to enhance the performance of PSO. As a result, the efficiency of convergence speed and solution accuracy can be maximized. The experimental results show that the ACPSO outperforms More >

  • Open Access


    State-Based Control Feature Extraction for Effective Anomaly Detection in Process Industries

    Ming Wan1, Jinfang Li1, Jiangyuan Yao2, *, Rongbing Wang1, 3, Hao Luo1

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1415-1431, 2020, DOI:10.32604/cmc.2020.09692

    Abstract In process industries, the characteristics of industrial activities focus on the integrality and continuity of production process, which can contribute to excavating the appropriate features for industrial anomaly detection. From this perspective, this paper proposes a novel state-based control feature extraction approach, which regards the finite control operations as different states. Furthermore, the procedure of state transition can adequately express the change of successive control operations, and the statistical information between different states can be used to calculate the feature values. Additionally, OCSVM (One Class Support Vector Machine) and BPNN (BP Neural Network), which are More >

  • Open Access


    Implementation of PSOANN Optimized PI Control Algorithm for Shunt Active Filter

    M. Sujith1, *, S. Padma2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.3, pp. 863-888, 2020, DOI:10.32604/cmes.2020.08908

    Abstract This paper proposes the optimum controller for shunt active filter (SAF) to mitigate the harmonics and maintain the power quality in the distribution system. It consists of shunt active filter, Voltage Source Inverter (VSI), series inductor and DC bus and nonlinear load. The proposed hybrid approach is a combination of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) termed as PSOANN. The PI controller gain parameters of kp and ki are optimized with the help of PSOANN. The PSOANN improves the accuracy of tuning the gain parameters under steady and dynamic load conditions; thereby it… More >

  • Open Access


    Seasonal testicular changes in Dendropsophus minutus Peters, 1872 (Anura, Hylidae)


    BIOCELL, Vol.36, No.2, pp. 57-62, 2012, DOI:10.32604/biocell.2012.36.057

    Abstract The reproductive cycle in anurans may be either continuous or discontinuous. These differences may be connected to seasonal climate changes and/or to anthropic activity. Forty adult male individuals of the Dendropsophus minutus species were collected during one year, in the municipality of Chapada dos Guimarães (Mato Grosso, Brazil). The testicles were studied under light and transmission electron microscopy. No variations were observed when the diameter of the seminiferous tubules and the thickness of the interstitial tissue were studied. However, changes in spermatogenesis were conspicuous and indicated that the reproductive cycle of D. minutus in Chapada dos Guimarães More >

  • Open Access


    SVM Model Selection Using PSO for Learning Handwritten Arabic Characters

    Mamouni El Mamoun1,*, Zennaki Mahmoud1, Sadouni Kaddour1

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 995-1008, 2019, DOI:10.32604/cmc.2019.08081

    Abstract Using Support Vector Machine (SVM) requires the selection of several parameters such as multi-class strategy type (one-against-all or one-against-one), the regularization parameter C, kernel function and their parameters. The choice of these parameters has a great influence on the performance of the final classifier. This paper considers the grid search method and the particle swarm optimization (PSO) technique that have allowed to quickly select and scan a large space of SVM parameters. A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model. SVM is More >

  • Open Access


    Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

    N. Kins Burk Sunil1, *, R. Ganesan2, B. Sankaragomathi3

    CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.2, pp. 351-375, 2019, DOI:10.31614/cmes.2018.04484

    Abstract Obstructive Sleep Apnea (OSA) is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation. The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea (SA) activity. In the proposed method, the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted. These features are applied to the Classification and Regression Tree (CART)-Particle Swarm Optimization (PSO) classifier which classifies the More >

  • Open Access


    Homogenization Analysis for Particulate Composite Materials using the Boundary Element Method

    Hiroshi Okada1, Yasuyoshi Fukui1, Noriyoshi Kumazawa1

    CMES-Computer Modeling in Engineering & Sciences, Vol.5, No.2, pp. 135-150, 2004, DOI:10.3970/cmes.2004.005.135

    Abstract A method to obtain the effective mechanical properties of particulate composite materials is presented in this paper. The methodology is based on the boundary element method (BEM) coupled with analytical solutions for ellipsoidal inclusions such as Eshelby's tensor. There is no numerical integration for the surfaces or the domains of distributed particles, and, therefore, proposed technique is very efficient. Homogenization analysis based on representative volume element (RVE) is carried out considering a unit cell containing many particles (up to 1000). By using a conventional BEM approach (i.e., multi-region BEM), it would be extremely difficult to More >

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