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Radial basis function neural network
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This research focuses on the integration of a radial basis function neural network (RBFNN) for uncertainty approximation in pneumatic artificial muscle (PAM) systems within the framework of power rate exponential reaching law sliding mode control (PRERL-SMC). Configured in an antagonistic manner, PAMs provide a range of benefits for developing actuators with human-like characteristics.
9p
vimichaelfaraday
14-12-2023
9
4
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Estimation of Construction Price Index (CPI) is important for a market economy and it is a measure to manage construction investment costs. This is a tool to help organizations and individuals to reduce the effort and management of expenses for construction projects by reducing time of procedures for calculating and adjusting the total investment for the estimation and evaluation of contract price.
11p
visharma
20-10-2023
7
4
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Ebook "Neural networks - A comprehensive foundation" includes content: Introduction, learning processes; single layer perceptrons; multilayer perceptrons; radial basis function networks; support vector machines; committee machines; principal components analysis; self organizing maps; information theoretic models; stochastic machines and their approximates rooted in statistical mechanics; neurodynamic programming; temporal processing using feedforward networks; neurodynamics; dynamically driven recurrent networks.
823p
haojiubujain07
20-09-2023
6
2
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Ebook "An introduction to Neural network methods for differential equations" introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations.
124p
dieptieuung
19-07-2023
281
274
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In this paper, Wavelet transforms for the recognition and localization of short circuits faults on the power transmission lines. In that, the voltage waves and current waves on the lines are simulated by Simulink - Matlab.
5p
vidoctorstrange
06-05-2023
5
3
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This paper confirms the practical effect of applying Artificial Neural Networks (ANNs) using Radial basis function (RBF) bases on Sliding mode control (SMC) to control nonlinear systems. The proposed algorithm is put into comparison with the super twisting 2-SMC, which was designed to reduce chattering and increase the performance of conventional SMC.
7p
vidoctorstrange
06-05-2023
9
5
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In this paper, we consider the adaptive sliding mode control with radial basis function neural networks for the Omni-directional mobile robot. This is a holonomic robot that can operate easily in small and narrow spaces, due to the ability of flexible rotational and translational moving, simultaneously and independently.
8p
vidoctorstrange
06-05-2023
12
6
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Bài viết Điều khiển trượt hệ nâng vật trong từ trường dùng mạng nơ ron hàm cơ sở xuyên tâm được nghiên cứu nhằm mục tiêu áp dụng bộ điều khiển trượt dùng mạng nơ-ron hàm cơ sở xuyên tâm, gọi tắt là mạng nơron RBF (Radial Basis Function Neural Networks) cho hệ nâng vật trong từ trường.
5p
visaleen
30-10-2022
13
4
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Ebook "Neural network and deep learning: A textbook" provide readers with content about: an introduction to neural networks; machine learning with shallow neural networks; training deep neural networks; teaching deep learners to generalize; radial basis function networks;...
512p
tieuduongchi
07-10-2022
13
6
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This present study proposes a design and the analysis of the novel adaptive robust neural networks (ARNNs) based on the backstepping control method for industrial robot manipulators (IRMs). In this research, the ARNNs controller has combined the advantages of Radial Basis Function neural network (RBFNN), the robust term, and adaptive backstepping control technique without the requirement of prior knowledge.
7p
vigeneralmotors
13-07-2022
471
10
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The paper has developed an adaptive algorithm using neural network for controlling dual-arm robotic system in stable holding a rectangle object and moving it to track the desired trajectories. Firstly, an overall dynamic of the system including the dual-arm robot and the object is derived based on Euler-Lagrangian principle.
7p
visherylsandber
04-07-2022
8
2
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This paper presents a novel adaptive controller for two-wheeled selfbalancing mobile robots combining sliding mode control and hierarchical sliding control techniques. In addition, the radial basis function neural networks (RBFNN) are also applied to approximate the uncertain components in the system.
9p
viericschmid
12-01-2022
23
3
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The paper has developed an adaptive control using neural network for controlling a dual-arm robotic system in moving a rectangle object to the desired trajectories. Firstly, the overall dynamics of the manipulators and the object have been derived based on Euler-Lagrangian principle. And then based on the dynamics, a controller has been proposed to achieve the desired trajectories of the grasping object
9p
spiritedaway36
28-11-2021
35
5
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Identification of acute or recent hepatitis C virus (HCV) infections is important for detecting outbreaks and devising timely public health interventions for interruption of transmission. Epidemiological investigations and chemistry-based laboratory tests are 2 main approaches that are available for identification of acute HCV infection. However, owing to complexity, both approaches are not efficient.
10p
vilarryellison
29-10-2021
9
0
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In this paper, a robust hierarchical method for trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) subjected to parameter uncertainties and external disturbances is presented. A robust control scheme based on a fast nonsingular terminal sliding mode strategy is designed to achieve fast response and excellent tracking accuracy. Moreover, a radial basis function artificial neural network with online adaptive schemes to estimate unknown aerodynamic parameters and external disturbances is developed to improve the control performance and reduce the chattering phenomenon.
6p
cothumenhmong11
05-05-2021
14
3
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Bài giảng Lý thuyết nhận dạng - Một số kỹ thuật trong lý thuyết nhận dạng (tiếp) với các nội dung radial basis functions neural networks; kiến trúc của mạng neural RBF; khớp đường cong sử dụng mạng neural RBF; biểu diễn của dữ liệu nói trên; phân bố của mẫu trong ví dụ...
76p
cothumenhmong7
05-09-2020
45
5
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This article highlights a robust adaptive tracking control approach for a nonholonomic wheeled mobile robot by which the bad problems of both unknown slippage and uncertainties are dealt with. The radial basis function neural network in this proposed controller assists unknown smooth nonlinear dynamic functions to be approximated. Furthermore, a technical solution is also carried out to avoid actuator saturation. The validity and efficiency of this novel controller, finally, are illustrated via comparative simulation results.
18p
nguyenanhtuan_qb
18-06-2020
27
2
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This study presents an adaptive algorithm for performance assessment of project management with respect to resilience engineering and job security in a large construction site.
16p
kelseynguyen
27-05-2020
24
2
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Bài báo này trình bày phương pháp nhận dạng hệ thống phi tuyến MIMO (MultipleInput-Multiple-Output) sử dụng mạng nơ - ron RBF (Radial Basis Function Neural Networks). Phương pháp này được ứng dụng để nhận dạng đối tượng robot di động đa hướng (Omni-Directional Mobile Robot). Đây là một loại robot holonomic có thể di chuyển dễ dàng trong những không gian nhỏ, hẹp do khả năng di chuyển một cách linh hoạt, vừa quay vừa tịnh tiến đồng thời và độc lập.
5p
slimzslimz
17-12-2019
71
3
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This paper proposes an adaptive control method based on sliding-mode control (SMC) technique for the 3D overhead crane system when considering that the mass of payload unknown. Constant-type and function-type adaptation mechanisms are integrated into control law, in which the mass of payload is considered as a constant-type uncertainty, whereas the uncertain dynamical functionw are estimated by radial basis function neural networks (RBFNNs).
13p
visumika2711
17-07-2019
33
1
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