Neurocomputing approaches to modelling of drying process dynamics

被引：43

作者：

Kaminski, W ^{[1
]}

Strumillo, P ^{[1
]}

Tomczak, E ^{[1
]}

机构：

[1] Lodz Tech Univ, Fac Proc & Environm Engn, PL-90924 Lodz, Poland

来源：

DRYING TECHNOLOGY | 1998年 / 16卷 / 06期

关键词：

artificial neural networks; degradation kinetics; drying kinetics; genetic algorithms; multilayer perceptron; radial basis functions; smoothing; system modelling in process engineering;

D O I：

10.1080/07373939808917450

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The application of artificial neural networks to mathematical modelling of drying kinetics, degradation kinetics and smoothing of experimental data is discussed in the paper. A theoretical foundation of drying process description by means of artificial neural networks is presented. Two network types are proposed for drying process modelling. namely the multilayer perceptron network and the radial basis functions network. These were validated experimentally for fresh green peas and diced potatoes which represent diverse food products. Network training procedures based on experimental data are explained. Additionally, the proposed neural network modelling approach is tested on drying experiments of silica gel saturated with ascorbic acid solution.

引用

页码：967 / 992

页数：26

共 23 条

[1]

Adamiec J., 1995, HDB IND DRYING, VII, P775

[2]

[Anonymous], 1976, TIME SERIES ANAL

[3]

[Anonymous], 1991, Handbook of genetic algorithms

[4]

Bulsari A.B., 1995, NEURAL NETWORKS CHEM

[5]

BULSARI AB, 1996, SOLVING ENG PROBLEMS

[6] AN INTRODUCTION TO SIMULATED EVOLUTIONARY OPTIMIZATION [J].

FOGEL, DB .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (01) :3-14

[7]

HAGEN MT, 1994, IEEE T NEURAL NETWOR, V5, P989

[8]

Haykin S, 1998, NEURAL NETWORKS COMP

[9]

HEYD B, 1996, P 10 INT DRYING S ID, P323

[10]

HUANG B, 1993, DRYING TECHNOLOGY, V11, P524

← 1 2 3 →