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| G.I. Papadimitriou, "Hierarchical Discretized Pursuit Nonlinear Learning Automata with Rapid Convergence and High Accuracy," IEEE Transactions on Knowledge and Data Engineering, vol. 6, no. 4, pp. 654-659, August, 1994. | |||
| BibTex | x | ||
| @article{ 10.1109/69.298184, author = {G.I. Papadimitriou}, title = {Hierarchical Discretized Pursuit Nonlinear Learning Automata with Rapid Convergence and High Accuracy}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {6}, number = {4}, issn = {1041-4347}, year = {1994}, pages = {654-659}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.298184}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Knowledge and Data Engineering TI - Hierarchical Discretized Pursuit Nonlinear Learning Automata with Rapid Convergence and High Accuracy IS - 4 SN - 1041-4347 SP654 EP659 EPD - 654-659 A1 - G.I. Papadimitriou, PY - 1994 KW - finite automata; unsupervised learning; nonlinear systems; convergence; hierarchical systems; rapid convergence; absorbing multiaction learning automaton; discretized pursuit nonlinear learning automata; hierarchical tree; stationary stochastic environment; pursuit learning; nonlinear output function; epsilon-optimal learning; positioning algorithm VL - 6 JA - IEEE Transactions on Knowledge and Data Engineering ER - | |||
A new absorbing multiaction learning automaton that is epsilon-optimal is introduced. It is a hierarchical discretized pursuit nonlinear learning automaton that uses a new algorithm for positioning the actions on the leaves of the hierarchical tree. The proposed automaton achieves the highest performance (speed of convergence, central processing unit (CPU) time, and accuracy) among all the absorbing learning automata reported in the literature up to now. Extensive simulation results indicate the superiority of the proposed scheme. Furthermore, it is proved that the proposed automaton is epsilon-optimal in every stationary stochastic environment.
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