Learning Multiple Goal-Directed Actions Through Self-Organization of a Dynamic Neural Network Model: A Humanoid Robot Experiment

2008 ◽  
Vol 16 (2-3) ◽  
pp. 166-181 ◽  
Author(s):  
Ryunosuke Nishimoto ◽  
Jun Namikawa ◽  
Jun Tani
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Humanoid Robot ◽  
Self Organization ◽  
Dynamic Neural Network ◽  
Multiple Goal

An Improved RBF Neural Network for Short Term Load Forecasting

2014 ◽  
Vol 1008-1009 ◽  
pp. 709-713 ◽  
Author(s):  
Chuang Li ◽  
Zhi Qiang Liang ◽  
Min You Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Discriminant Function ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Static Model ◽  
Short Term ◽  
Dynamic Neural Network ◽  
Short Term Load Forecasting

Neural network is widely used in the load forecasting area,but the traditional methods of load forecasting usually base on static model,which cannot change as time goes on. And the accuracy is worse and worse. To solve the problem, a dynamic neural network model for load forecasting is proposed .By way of introduce Error discriminant function, to control the error of load forecasting and dynamically modify the predicting model. Through the contrast of the short-term load forecasting result based on static neural network model and dynamic neural network model proposed, the error of load forecasting is decrease effectively.

Analog Neural Network Model Produces Chaos Similar to the Human EEG

1997 ◽  
Vol 07 (05) ◽  
pp. 1133-1140 ◽  
Author(s):  
Vladimir E. Bondarenko
Keyword(s):  
Neural Network ◽  
Lyapunov Exponent ◽  
Network Model ◽  
Shannon Entropy ◽  
Neural Network Model ◽  
Correlation Dimension ◽  
The Self ◽  
Self Organization ◽  
Largest Lyapunov Exponent ◽  
Human Eeg

The self-organization processes in an analog asymmetric neural network with the time delay were considered. It was shown that in dependence on the value of coupling constants between neurons the neural network produced sinusoidal, quasi-periodic or chaotic outputs. The correlation dimension, largest Lyapunov exponent, Shannon entropy and normalized Shannon entropy of the solutions were studied from the point of view of the self-organization processes in systems far from equilibrium state. The quantitative characteristics of the chaotic outputs were compared with the human EEG characteristics. The calculation of the correlation dimension ν shows that its value is varied from 1.0 in case of sinusoidal oscillations to 9.5 in chaotic case. These values of ν agree with the experimental values from 6 to 8 obtained from the human EEG. The largest Lyapunov exponent λ calculated from neural network model is in the range from -0.2 s -1 to 4.8 s -1 for the chaotic solutions. It is also in the interval from 0.028 s -1 to 2.9 s -1 of λ which is observed in experimental study of the human EEG.

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