Neural Decoding Forelimb Trajectory Using Evolutionary Neural Networks with Feedback-Error-Learning Schemes

2018 ◽  
Author(s):  
Yu-Chieh Lin ◽  
Chin Chou ◽  
Shin-Hung Yang ◽  
Hsin-Yi Lai ◽  
Yu-Chun Lo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Neural Decoding ◽  
Evolutionary Neural Networks ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Learning Schemes

scholarly journals A Novel Method for Training an Echo State Network with Feedback-Error Learning

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Rikke Amilde Løvlid
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Training Data ◽  
Echo State Network ◽  
Training Method ◽  
Computationally Efficient ◽  
Echo State Networks ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Echo state networks are a relatively new type of recurrent neural networks that have shown great potentials for solving non-linear, temporal problems. The basic idea is to transform the low dimensional temporal input into a higher dimensional state, and then train the output connection weights to make the system output the target information. Because only the output weights are altered, training is typically quick and computationally efficient compared to training of other recurrent neural networks. This paper investigates using an echo state network to learn the inverse kinematics model of a robot simulator with feedback-error-learning. In this scheme teacher forcing is not perfect, and joint constraints on the simulator makes the feedback error inaccurate. A novel training method which is less influenced by the noise in the training data is proposed and compared to the traditional ESN training method.

scholarly journals Robot Manipulators Control with Guaranteed Stability Using Feedback Error Learning Neural Networks

1996 ◽  
Vol 8 (4) ◽  
pp. 383-391
Author(s):  
Ju-Jang Lee ◽  
◽  
Sung-Woo Kim ◽  
Kang-Bark Park
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Rule ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network ◽  
Control Schemes ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Among various neural network learning control schemes, feedback error learning(FEL)8),9) has been known that it has advantages over other schemes. However, such advantages are founded on the assumption that the systems is linearly parameterized and stable. Thus, FEL has difficulties in coping with uncertain and unstable systems. Furthermore, it is not clear how the learning rule of FEL is obtained in the minimization sense. Therefore, to overcome such problems, we propose neural network control schemes using FEL with guaranteed performance. The proposed strategy is to use multi-layer neural networks, to design a stabilityguaranteeing controller(SGC), and to derive a learning rule to obtain the tracking performance. Using multilayer neural networks we can fully utilize the learning capability no matter how the system is linearly parameterized or not. The SGC makes it possible for the neural network to learn without fear of instability. As a result, the more the neural network learning proceeds, the better the tracking performance becomes.

scholarly journals Applying RBF Neural Nets for Position Control of an Inter/Scara Robot

2009 ◽  
Vol 4 (2) ◽  
pp. 148
Author(s):  
Fernando Passold
Keyword(s):  
Neural Networks ◽  
Position Control ◽  
Sliding Mode ◽  
Neural Nets ◽  
Rbf Neural Networks ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
On Line ◽  
Error Learning ◽  
Manipulator Robot

This paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on the feedback error learning architecture. The main advantage of this architecture is that it does not require any modification of the previous conventional controller algorithm. MLP and RBF neural networks trained on-line have been used, without requiring any previous knowledge about the system to be controlled. These approach has performed very successfully, with better results obtained with the RBF networks when compared to PID and sliding mode positional controllers.

Nonlinear Discrete-time feedback error learning with PI Controller for AC servo motor

2008 ◽  
Author(s):  
Noppanan Suwanjatuporn ◽  
Mes Napaamporn ◽  
Waree Kongprawechnon ◽  
Sirisak Wongsura
Keyword(s):  
Discrete Time ◽  
Pi Controller ◽  
Servo Motor ◽  
Ac Servo ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Ac Servo Motor
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