This paper presents a new procedure for optimization of continuous mixed suspensionmixed product removal (MSMPR) crystallizing systems. Owing to the difficulties of theoretical modelling, simulation of the MSMPR crystallization process is based on the use of artificial neural networks (ANN). The opt
β¦ LIBER β¦
Gradientless shape optimization using artificial neural networks
β Scribed by Krishna K. Pathak; D. K. Sehgal
- Publisher
- Springer-Verlag
- Year
- 2009
- Tongue
- English
- Weight
- 362 KB
- Volume
- 41
- Category
- Article
- ISSN
- 1615-1488
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Crystallization process optimization usi
β
Prof. Dr. Ir. Alexandru Woinaroschy; Lect. Ir. Raluca Isopescu; Prof. Dr. Ir. La
π
Article
π
1994
π
John Wiley and Sons
π
English
β 280 KB
π 2 views
Integrated optimal topology design and s
β
A.R. Yildiz; N. ΓztΓΌrk; N. Kaya; F. ΓztΓΌrk
π
Article
π
2003
π
Springer-Verlag
π
English
β 749 KB
Sentence recognition using artificial ne
β
Maciej Majewski; Jacek M. Zurada
π
Article
π
2008
π
Elsevier Science
π
English
β 275 KB
The paper describes an application of artificial neural networks (ANN) for natural language text reasoning. The task of knowledge discovery in text from a database, represented with a database file consisting of sentences with similar meanings but different lexico-grammatical patterns, was solved wi
Pattern recognition using artificial neu
β
R.S.H. Mah; V. Chakravarthy
π
Article
π
1992
π
Elsevier Science
π
English
β 655 KB
Fracture prediction using artificial neu
β
J. E. B. Jensen; P. K. Sharpe; P. Caleb; H. A. SΓΈrensen
π
Article
π
1996
π
Springer-Verlag
π
English
β 147 KB
Mapping artificial neural networks to a
β
Harri Klapuri; Timo HΓ€mΓ€lΓ€inen; Jukka Saarinen; Kimmo Kaski
π
Article
π
1996
π
Elsevier Science
π
English
β 922 KB