Neural computing in pharmaceutical products and process development
Само за регистроване кориснике
2013
Поглавље у монографији (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
This chapter presents a review of the possible applications of methods based on neural computing in pharmaceutical products and process development. Some of the methods described are used for classification purposes, whereas others can be applied to modeling and optimization, or even induction of rules. Basic concepts of each method are theoretically described, followed by examples of their application in pharmaceutical technology. A theoretical background aims to provide a better understanding of the methods and is based upon their most important features. Examples should encourage the reader to embrace the above-mentioned methods and use them to complement conventional statistical methods for classification and regression.
Кључне речи:
Artificial neural networks / Decision trees / Evolutionary computing and genetic algorithms / Fuzzy logic / Neural computing / Self-organizing mapsИзвор:
Computer-Aided Applications in Pharmaceutical Technology, 2013, 91-175Издавач:
- Elsevier Inc.
Институција/група
PharmacyTY - CHAP AU - Đuriš, Jelena AU - Ibrić, Svetlana AU - Đurić, Zorica PY - 2013 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2024 AB - This chapter presents a review of the possible applications of methods based on neural computing in pharmaceutical products and process development. Some of the methods described are used for classification purposes, whereas others can be applied to modeling and optimization, or even induction of rules. Basic concepts of each method are theoretically described, followed by examples of their application in pharmaceutical technology. A theoretical background aims to provide a better understanding of the methods and is based upon their most important features. Examples should encourage the reader to embrace the above-mentioned methods and use them to complement conventional statistical methods for classification and regression. PB - Elsevier Inc. T2 - Computer-Aided Applications in Pharmaceutical Technology T1 - Neural computing in pharmaceutical products and process development SP - 91 EP - 175 DO - 10.1016/B978-1-907568-27-5.50005-6 ER -
@inbook{ author = "Đuriš, Jelena and Ibrić, Svetlana and Đurić, Zorica", year = "2013", abstract = "This chapter presents a review of the possible applications of methods based on neural computing in pharmaceutical products and process development. Some of the methods described are used for classification purposes, whereas others can be applied to modeling and optimization, or even induction of rules. Basic concepts of each method are theoretically described, followed by examples of their application in pharmaceutical technology. A theoretical background aims to provide a better understanding of the methods and is based upon their most important features. Examples should encourage the reader to embrace the above-mentioned methods and use them to complement conventional statistical methods for classification and regression.", publisher = "Elsevier Inc.", journal = "Computer-Aided Applications in Pharmaceutical Technology", booktitle = "Neural computing in pharmaceutical products and process development", pages = "91-175", doi = "10.1016/B978-1-907568-27-5.50005-6" }
Đuriš, J., Ibrić, S.,& Đurić, Z.. (2013). Neural computing in pharmaceutical products and process development. in Computer-Aided Applications in Pharmaceutical Technology Elsevier Inc.., 91-175. https://doi.org/10.1016/B978-1-907568-27-5.50005-6
Đuriš J, Ibrić S, Đurić Z. Neural computing in pharmaceutical products and process development. in Computer-Aided Applications in Pharmaceutical Technology. 2013;:91-175. doi:10.1016/B978-1-907568-27-5.50005-6 .
Đuriš, Jelena, Ibrić, Svetlana, Đurić, Zorica, "Neural computing in pharmaceutical products and process development" in Computer-Aided Applications in Pharmaceutical Technology (2013):91-175, https://doi.org/10.1016/B978-1-907568-27-5.50005-6 . .