Adaptive Inventory Control Based on Fuzzy Neural Network under Uncertain Environment

Joint Authors

Zhang, Songtao
Ge, Jianqiao

Source

Complexity

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-07-29

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Philosophy

Abstract EN

In order to achieve the actual inventory effectively tracking the target inventory under uncertain environment, this paper investigates an adaptive inventory controller for the production-inventory system.

First, an uncertain production-inventory model is constructed, and then, the uncertainty of the production-inventory model is approximated by a fuzzy neural network.

Secondly, in terms of the design of adaptive control law, the adaptive inventory controller is developed.

Under the adaptive inventory controller, the actual inventory can track the target inventory in real time and the production-inventory system can be robustly stable in uncertain environment.

Finally, the results of three simulation experiments show that the proposed adaptive inventory controller can realize both the fast tracking speed and the high tracking accuracy.

American Psychological Association (APA)

Ge, Jianqiao& Zhang, Songtao. 2020. Adaptive Inventory Control Based on Fuzzy Neural Network under Uncertain Environment. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1142798

Modern Language Association (MLA)

Ge, Jianqiao& Zhang, Songtao. Adaptive Inventory Control Based on Fuzzy Neural Network under Uncertain Environment. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1142798

American Medical Association (AMA)

Ge, Jianqiao& Zhang, Songtao. Adaptive Inventory Control Based on Fuzzy Neural Network under Uncertain Environment. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1142798

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1142798