AIMC Topic: Binomial Distribution

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Nonfragile H State Estimation for Recurrent Neural Networks With Time-Varying Delays: On Proportional-Integral Observer Design.

IEEE transactions on neural networks and learning systems Aug 3, 2021
In this article, a novel proportional-integral observer (PIO) design approach is proposed for the nonfragile H state estimation problem for a class of discrete-time recurrent neural networks with time-varying delays. The developed PIO is equipped wit...
Humans Neural Networks, Computer Algorithms Computer Simulation Reproducibility of Results Least-Squares Analysis Equipment Design Probability Binomial Distribution
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Asynchronous dissipative filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts.

Neural networks : the official journal of the International Neural Network Society Jul 15, 2020
This work focuses on the problem of asynchronous filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts (VPDs). The discrete-time nonhomogeneous Markov process is adopted to depict the modes switching of target pl...
Neural Networks, Computer Computer Simulation Probability Markov Chains Binomial Distribution
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Improved result on state estimation for complex dynamical networks with time varying delays and stochastic sampling via sampled-data control.

Neural networks : the official journal of the International Neural Network Society Feb 22, 2019
This paper investigates state estimation for complex dynamical networks (CDNs) with time-varying delays by using sampled-data control. For the simplicity of technical development, only two different sampling periods are considered whose occurrence pr...
Neural Networks, Computer Time Factors Stochastic Processes Binomial Distribution
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Predictability and interpretability of hybrid link-level crash frequency models for urban arterials compared to cluster-based and general negative binomial regression models.

International journal of injury control and safety promotion Feb 24, 2017
Machine learning (ML) techniques have higher prediction accuracy compared to conventional statistical methods for crash frequency modelling. However, their black-box nature limits the interpretability. The objective of this research is to combine bot...
Humans Machine Learning Forecasting Cities Environment Design Cluster Analysis Models, Statistical Regression Analysis Data Interpretation, Statistical Accidents, Traffic Binomial Distribution
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