Title :
A study on the convergence of MEBML algorithms
Author :
Chuan-Long, Wang ; Jian-Guo, Zhang
Author_Institution :
Dept. of Math., Shanxi Univ., Taiyuan, China
Abstract :
The mind-evolution-based machine-learning (MEBML) algorithm involving a Markov chain is analyzed in continuous state space. By any finite interval approximation, the convergence of similar taxis operation is proved. Meanwhile, the local property of similar taxis operation is shown. To avoid its prematurity, dissimilation operation need to be introduced. With the concept of absorbing field and p-optimal state, the convergence of dissimilation operation is proved. Finally, the functions of similar taxis and dissimilation operations are analyzed with a view to practical application
Keywords :
Markov processes; convergence; evolutionary computation; learning (artificial intelligence); optimisation; state-space methods; MEBML; Markov chain; absorbing field; continuous state space; convergence; dissimilation; finite interval approximation; mind-evolution-based machine-learning; p-optimal state; similar taxis; Algorithm design and analysis; Blindness; Convergence; Educational institutions; Genetics; Mathematics; Optimization methods; Scattering; Space technology; State-space methods;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.859929
