Mendeley
TY  _  JOUR
ID  -  1394090812333624
TI  -  A New Statistical Characteristics Based Method for Adaptive Learning Rate Adjustment in Learning Automata
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Mollakhalili Meybodi M. R.
AU  -  meybodi meybodi
AD  -  دانشگاه آزاد اسلامی، واحد میبد
AD  -  
Y1  -  2013
PY  -  2013
VL  -  2
IS  -  
SP  -  43
EP  -  51
KW  -  Learning automata
dynamic learning rate
learning rate adjustment
Chebyshev’s inequality
DO  -  
N2  -  The value of learning rate and its change mechanisms is one of the issues in designing learning systems such as learning automata. In most cases a time-based reduction function is used to adjust the learning rate aim at reaching stability in training system. So the learning rate is a parameter that determines to what extent a learning system is based on past experiences, and the impact of current events on it. This method is efficient but does not properly function in dynamic and non-stationary environments.
In this paper, a new method for adaptive learning rate adjustment in learning automata is proposed. In this method, in addition to the length of time to learn, some statistical characteristics of actions probability vector of Learning Automata are used to determine the increase or decrease of learning rate. Furthermore, unlike existing methods, during the process of learning, both increase and decrease of the learning rate is done and Learning Automata responds effectively to changes in the dynamic random environment.
Empirical studies show that the proposed method has more flexibility in compatibility to the non-stationary dynamic environments and get out of local maximum points and the learned values are closer to the true values.
UR  - rimag.ir/en/Article/28057
L1  - rimag.ir/en/Article/Download/28057
ER  -  
