.png){kind=icon}
Return
Dynamic irregular cellular learning automata
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jocs.2017.08.012.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Irregular Cellular learning automaton (ICLA), which is recently introduced, is a cellular learning automaton (CIA) with irregular structure. ICLA is suitable for modeling problems which are not regular in nature, such as problems in the area of sensor networks, web mining, and grid computing. In some areas such as mobile ad hoc and sensor networks, where the structure of the environment changes over the time, an ICLA with a dynamic structure is required. For this reason, in this paper, we have extended ICLA in such a way that the structure of the extended model, called dynamic ICLA (DICLA), can change over time. For the newly introduced model, we have proposed the concept of expediency and then, discussed sufficient conditions under which a DICLA becomes expedient. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Learning automata
Cellular learning automata
Irregular cellular learning automata
Dynamic irregular cellular learning automata
Expediency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
18.3
Papers:
3.1K
Citations:
4.0K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSave
ShareSaveLiving object detection system based on comb pattern capacitive sensor for wireless EV chargers
PREAI
ShareSaveShareSaveShareSave
View more