Add to ORKG67 p.Neural networks are widely used in industry fields, like robotic and process controllers. Extensive research and progress have also been done in various kinds of neural network algorithm, for example, the BP network, Hopfield network, RBF network, and a class of robust neural network algorithms. This thesis focuses on studying and developing robust online training and pruning algorithms for neural network tracking control systems. In particular, a complete convergence analysis is presented for Robust Adaptive Dead Zone training algorithm and Robust Adaptive Gradient Decent training algorithm, respectively.Master of Science (Computer Control and Automation
In this paper, a novel robust training algorithm of multi-input multi-output recurrent neural networ...
Abstract. It is normally difficult to determine the optimal size of neural networks, particularly, i...
We aim at the optimization of the tracking control of a robot to improve the robustness, under the e...
67 p.Neural networks are widely used in industry fields, like robotic and process controllers. Exten...
This thesis focuses on developing robust online training and pruning algorithms for a class of neura...
It is difficult to determine the number of nodes that should be used in a neural network. An adaptiv...
This thesis focuses on the study of the neural network (NN) and its application to robot tracking co...
A robust neural network is proposed for use with a proportional fixed control scheme for robot contr...
This paper presents an investigation on the trajectory control of a robot using a new type of recurr...
In this project, the robust adaptive controller has been surveyed to evaluate the guaranteed converg...
This article proposes an adaptive robust controller based on neural networks (NNs) for industrial ro...
Abstract: In this paper, a controller for robot manipulators is proposed to guarantee the track-ing ...
An adaptive neural network controller is brought forward by the paper to solve trajectory tracking p...
In this paper, a neural-network based robust adaptive controller is proposed to control an industria...
This dissertation is concerned with the development of neural network-based methods to the control o...
In this paper, a novel robust training algorithm of multi-input multi-output recurrent neural networ...
Abstract. It is normally difficult to determine the optimal size of neural networks, particularly, i...
We aim at the optimization of the tracking control of a robot to improve the robustness, under the e...
67 p.Neural networks are widely used in industry fields, like robotic and process controllers. Exten...
This thesis focuses on developing robust online training and pruning algorithms for a class of neura...
It is difficult to determine the number of nodes that should be used in a neural network. An adaptiv...
This thesis focuses on the study of the neural network (NN) and its application to robot tracking co...
A robust neural network is proposed for use with a proportional fixed control scheme for robot contr...
This paper presents an investigation on the trajectory control of a robot using a new type of recurr...
In this project, the robust adaptive controller has been surveyed to evaluate the guaranteed converg...
This article proposes an adaptive robust controller based on neural networks (NNs) for industrial ro...
Abstract: In this paper, a controller for robot manipulators is proposed to guarantee the track-ing ...
An adaptive neural network controller is brought forward by the paper to solve trajectory tracking p...
In this paper, a neural-network based robust adaptive controller is proposed to control an industria...
This dissertation is concerned with the development of neural network-based methods to the control o...
In this paper, a novel robust training algorithm of multi-input multi-output recurrent neural networ...
Abstract. It is normally difficult to determine the optimal size of neural networks, particularly, i...
We aim at the optimization of the tracking control of a robot to improve the robustness, under the e...