A rough neural network for material proportioning system

Author

Wu, Yawen ; Zhang, Chang-N

Author_Institution

Dept. of Comput. Sci., Regina Univ., Sask., Canada

Volume

2

fYear

2002

fDate

29 June-1 July 2002

Firstpage

1189

Abstract

A rough membership function neural network for raw material proportioning is presented in this paper. Approximation neurons and decision-based decider neurons have been used in the design of the rough neural classification system. Data obtained from simulations are used for neuron implementation and testing. The simulation results show that the outputs are very close to the target values. The network performs good control on the composition of mixed material throughout the test.

Keywords

cement industry; control system analysis; control system synthesis; decision tables; materials handling; mixing; neural nets; rough set theory; approximation neurons; cement plant raw material proportioning systems; decision tables; decision-based decider neurons; mixed material control composition; rough membership functions; rough neural classification systems; rough neural networks; rough neurons; rough sets; Building materials; Chemicals; Computational modeling; Computer science; Information systems; Neural networks; Neurons; Raw materials; Rough sets; Set theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Circuits and Systems and West Sino Expositions, IEEE 2002 International Conference on

Print_ISBN

0-7803-7547-5

Type

conf

DOI

10.1109/ICCCAS.2002.1178996

Filename

1178996