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  1. 1721

    Existence and Exponential Stability of Periodic Solution for a Class of Generalized Neural Networks with Arbitrary Delays by Yimin Zhang, Yongkun Li, Kuohui Ye

    Published 2009-01-01
    “…By the continuation theorem of coincidence degree and M-matrix theory, we obtain some sufficient conditions for the existence and exponential stability of periodic solutions for a class of generalized neural networks with arbitrary delays, which are milder and less restrictive than those of previous known criteria. …”
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  2. 1722

    Research on Energy-Efficient Building Design Using Target Function Optimization and Genetic Neural Networks by Youxiang Huan

    Published 2025-01-01
    “…This paper utilizes the EnergyPlus API to directly call the simulation engine from within the optimization algorithm. The genetic neural network algorithm iteratively modifies design parameters (e.g., building orientation, insulation levels etc) and evaluates the resulting energy performance using EnergyPlus. …”
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  3. 1723
  4. 1724

    A BP Neural Network Method for Grade Classification of Loose Damage in Semirigid Pavement Bases by Bei Zhang, Jianyang Liu, Yanhui Zhong, Xiaolong Li, Meimei Hao, Xiao Li, Xu Zhang, Xiaoliang Wang

    Published 2021-01-01
    “…Based on the finite-difference time-domain (FDTD) method, a backpropagation (BP) neural network identification method for loose damage of a semirigid base is presented. …”
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  5. 1725

    Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach by Mohammad Reza Zakerzadeh, Mohsen Firouzi, Hassan Sayyaadi, Saeed Bagheri Shouraki

    Published 2011-01-01
    “…Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. …”
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  6. 1726
  7. 1727

    Normalization of Deviant Behavior in Muc2+/+ Mice through Dietary Incorporation of Bacillus subtilis Spores by Maryana Morozova, Alexander Alekseev, Arsalan Saeidi, Ekaterina Litvinova

    Published 2023-12-01
    “…Changes in cytokines, serotonin, and tyrosine levels may mediate the normalizing impact of B. subtilis spores on Muc2+/+ mice behavior. …”
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  8. 1728

    H∞ Synchronization of Semi-Markovian Jump Neural Networks with Randomly Occurring Time-Varying Delays by Mengping Xing, Hao Shen, Zhen Wang

    Published 2018-01-01
    “…Based on the Lyapunov stability theory, this paper mainly investigates the H∞ synchronization problem for semi-Markovian jump neural networks (semi-MJNNs) with randomly occurring time-varying delays (TVDs). …”
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  9. 1729

    A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US by Vladimir M. Krasnopolsky, Ying Lin

    Published 2012-01-01
    “…A novel multimodel ensemble approach based on learning from data using the neural network (NN) technique is formulated and applied for improving 24-hour precipitation forecasts over the continental US. …”
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  10. 1730
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  12. 1732

    Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network by Xihua Chen, Xing Yang

    Published 2022-01-01
    “…And it is realized by image semantic processing of colorimetric sensor array and deep convolutional neural network processing of imaging. And through the experimental experiments based on convolutional neural network image segmentation processing, the results show that the efficiency of extracting features corresponding to different layers in the convolutional neural network is that the extraction efficiency of feature 1 and feature 2 is higher in the processing of 4 layers. …”
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  13. 1733

    Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive Effect by Guowei Yang, Yonggui Kao, Changhong Wang

    Published 2013-01-01
    “…This paper considers dynamical behaviors of a class of fuzzy impulsive reaction-diffusion delayed cellular neural networks (FIRDDCNNs) with time-varying periodic self-inhibitions, interconnection weights, and inputs. …”
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  14. 1734

    Convergence and Stability of the Split-Step θ-Milstein Method for Stochastic Delay Hopfield Neural Networks by Qian Guo, Wenwen Xie, Taketomo Mitsui

    Published 2013-01-01
    “…A new splitting method designed for the numerical solutions of stochastic delay Hopfield neural networks is introduced and analysed. Under Lipschitz and linear growth conditions, this split-step θ-Milstein method is proved to have a strong convergence of order 1 in mean-square sense, which is higher than that of existing split-step θ-method. …”
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  15. 1735

    Stability of Impulsive Cohen-Grossberg Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms by Jinhua Huang, Jiqing Liu, Guopeng Zhou

    Published 2013-01-01
    “…This work concerns the stability of impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms as well as Dirichlet boundary condition. …”
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  16. 1736

    Freeze-Thaw Resistance of Normal and High Strength Concretes Produced with Fly Ash and Silica Fume by Cenk Karakurt, Yıldırım Bayazıt

    Published 2015-01-01
    “…This study is based on determination of the freeze-thaw resistance of air-entrained and non-air-entrained normal strength concrete (NC) and high strength concrete (HSC) produced with fly ash and silica fume according to surface scaling. …”
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  17. 1737
  18. 1738

    Investigation into the Prediction of Ship Heave Motion in Complex Sea Conditions Utilizing Hybrid Neural Networks by Yuchen Liu, Xide Cheng, Kunyu Han, Zhechun Liu, Baiwei Feng

    Published 2024-12-01
    “…Consequently, this paper proposes a hybrid neural network method that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory Networks (BiLSTMs), and an Attention Mechanism to predict the heaving motion of ships in moderate to complex sea conditions. …”
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