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AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design
Convolutional neural networks (CNNs) have demonstrated exceptional results in the
analysis of time-series data when used for Human Activity Recognition (HAR). The manual …
analysis of time-series data when used for Human Activity Recognition (HAR). The manual …
A critical review on control strategies for structural vibration control
In recent years, the application of structural control strategies to attenuate the dynamic
response of civil engineering structures subjected to human-induced and environmental …
response of civil engineering structures subjected to human-induced and environmental …
MFRFNN: Multi-functional recurrent fuzzy neural network for chaotic time series prediction
Chaotic time series prediction, a challenging research topic in dynamic system modeling,
has drawn great attention from researchers around the world. In recent years extensive …
has drawn great attention from researchers around the world. In recent years extensive …
Recurrent context layered radial basis function neural network for the identification of nonlinear dynamical systems
This paper proposes a novel recurrent context layered radial basis function neural network
(RCLRBFNN) for the identification of nonlinear dynamical systems. The proposed model …
(RCLRBFNN) for the identification of nonlinear dynamical systems. The proposed model …
Double internal loop higher-order recurrent neural network-based adaptive control of the nonlinear dynamical system
Controlling complex nonlinear dynamical systems using traditional methods has always
been a difficult task because the majority of systems seen in nature have intricate nonlinear …
been a difficult task because the majority of systems seen in nature have intricate nonlinear …
A novel radial basis function neural network with high generalization performance for nonlinear process modelling
Y Yang, P Wang, X Gao - Processes, 2022 - mdpi.com
A radial basis function neural network (RBFNN), with a strong function approximation ability,
was proven to be an effective tool for nonlinear process modeling. However, in many …
was proven to be an effective tool for nonlinear process modeling. However, in many …
A novel hybrid radial basis function method for predicting the fresh and hardened properties of self-compacting concrete
Z Nurlan - Advances in Engineering and Intelligence Systems, 2022 - aeis.bilijipub.com
It is observed from the published literature that there were so few studies concentrating on
predicting both fresh and hardened properties of self-compacting concrete (SCC). Hence, it …
predicting both fresh and hardened properties of self-compacting concrete (SCC). Hence, it …
Development of data-knowledge-driven predictive model and multi-objective optimization for intelligent optimal control of aluminum electrolysis process
Operational optimization of the Hall-Héroult cell is essential for achieving high efficiency and
cost-effectiveness in the aluminum electrolysis process. Due to the complicated mechanism …
cost-effectiveness in the aluminum electrolysis process. Due to the complicated mechanism …
[PDF][PDF] Modified Helicopters Turboshaft Engines Neural Network On-board Automatic Control System Using the Adaptive Control Method.
The work is devoted to the modification of helicopters turboshaft engines onboard automatic
control system of through the introduction of an adaptive control unit into it, which consists of …
control system of through the introduction of an adaptive control unit into it, which consists of …
An RBF neural network based on improved black widow optimization algorithm for classification and regression problems
H Liu, G Zhou, Y Zhou, H Huang, X Wei - Frontiers in …, 2023 - frontiersin.org
Introduction Regression and classification are two of the most fundamental and significant
areas of machine learning. Methods In this paper, a radial basis function neural network …
areas of machine learning. Methods In this paper, a radial basis function neural network …