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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 More >

  • Open Access

    REVIEW

    Overview of genetic causes of recurrent miscarriage and the diagnostic approach

    Tarek A ATIA

    BIOCELL, Vol.43, No.4, pp. 253-262, 2019, DOI:10.32604/biocell.2019.08180

    Abstract Recurring miscarriage (RM) is a frustrating reproductive complication with variable etiology. Numerous genetic defects have been known to play a crucial role in the etiology of RM. Chromosomal abnormalities are frequently detected, while other genetic defects cannot be diagnosed through routine research, such as cryptic chromosomal anomalies, single nucleotide polymorphism, single-gene defect, and gene copy number variation. Diagnostic laboratories have recently used variable advanced techniques to detect potential genetic abnormalities in couples with RM and/or in products of conception. Here we aim to summarize the known genetic causes of RM, with a focus on the More >

  • Open Access

    ARTICLE

    Smartphone User Authentication Based on Holding Position and Touch-Typing Biometrics

    Yu Sun1,2,*, Qiyuan Gao3, Xiaofan Du3, Zhao Gu3

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1365-1375, 2019, DOI:10.32604/cmc.2019.06294

    Abstract In this advanced age, when smart phones are the norm, people utilize social networking, online shopping, and even private information storage through smart phones. As a result, identity authentication has become the most critical security activity in this period of the intelligent craze. By analyzing the shortcomings of the existing authentication methods, this paper proposes an identity authentication method based on the behavior of smartphone users. Firstly, the sensor data and touch-screen data of the smart phone users are collected through android programming. Secondly, the eigenvalues of this data are extracted and sent to the More >

  • Open Access

    ARTICLE

    Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

    Jun Wu1,*, Kui Hu1, Yiwei Cheng2, Ji Wang1, Chao Deng2,*, Yuanhan Wang3

    Structural Durability & Health Monitoring, Vol.13, No.3, pp. 317-329, 2019, DOI:10.32604/sdhm.2019.05571

    Abstract Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds More >

  • Open Access

    ARTICLE

    Remaining Useful Life Prediction of Rolling Bearings Based on Recurrent Neural Network

    Yimeng Zhai1, Aidong Deng1,*, Jing Li1,2, Qiang Cheng1, Wei Ren3

    Journal on Artificial Intelligence, Vol.1, No.1, pp. 19-27, 2019, DOI:10.32604/jai.2019.05817

    Abstract In order to acquire the degradation state of rolling bearings and achieve predictive maintenance, this paper proposed a novel Remaining Useful Life (RUL) prediction of rolling bearings based on Long Short Term Memory (LSTM) neural net-work. The method is divided into two parts: feature extraction and RUL prediction. Firstly, a large number of features are extracted from the original vibration signal. After correlation analysis, the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model. In the part of RUL prediction, LSTM that making full use of More >

  • Open Access

    ARTICLE

    Delay-dependent Stability of Recurrent Neural Networks with Time-varying Delay

    Guobao Zhanga,b, Jing-Jing Xionga,b, Yongming Huanga,b, Yong Lua,b,c, Ling Wanga,b

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 541-551, 2018, DOI:10.31209/2018.100000021

    Abstract This paper investigates the delay-dependent stability problem of recurrent neural networks with time-varying delay. A new and less conservative stability criterion is derived through constructing a new augmented Lyapunov-Krasovskii functional (LKF) and employing the linear matrix inequality method. A new augmented LKF that considers more information of the slope of neuron activation functions is developed for further reducing the conservatism of stability results. To deal with the derivative of the LKF, several commonly used techniques, including the integral inequality, reciprocally convex combination, and free-weighting matrix method, are applied. Moreover, it is found that the obtained More >

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