@article {10.3844/jcssp.2012.965.970,
article_type = {journal},
title = {Advanced Logistic Belief Neural Network Algorithm for Robot Arm Control},
author = {Zacharie, Mbaitiga},
volume = {8},
number = {6},
year = {2012},
month = {Apr},
pages = {965-970},
doi = {10.3844/jcssp.2012.965.970},
url = {https://thescipub.com/abstract/jcssp.2012.965.970},
abstract = {Problem statement: This study discusses the implementation of advanced logistic belief Neural Network for robot arms control. Approach: Given the desired trajectory of the end-effectors in space, the logistic function is used to compute the conditional probability of the neurons being active in response to its induced field. The computations of conditional probabilities are performed under two different null conditions. (1) for all vectors not belonging to the parent of element node i and node j. (2) for node i greater than node j, which follows from the fact that the network is acyclic. Results: The test results proved the merit of the proposed method due to the fact that the robot arms move in the expected desired trajectory position within the allocated time set for each action. Conclusion/Recommendation: Our future work will be to improve this method for its use in the industrial robot arms.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}