1 resultado para OPTIMAL-GROWTH TEMPERATURES
em Boston University Digital Common
Filtro por publicador
- Aberystwyth University Repository - Reino Unido (3)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (2)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (1)
- Aquatic Commons (22)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (2)
- Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer (1)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (3)
- Aston University Research Archive (9)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (9)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (5)
- Bioline International (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (10)
- Boston University Digital Common (1)
- Brock University, Canada (2)
- Bulgarian Digital Mathematics Library at IMI-BAS (1)
- CaltechTHESIS (1)
- Cambridge University Engineering Department Publications Database (15)
- CentAUR: Central Archive University of Reading - UK (29)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (88)
- Cochin University of Science & Technology (CUSAT), India (5)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Deakin Research Online - Australia (16)
- Digital Commons - Michigan Tech (3)
- Digital Commons at Florida International University (5)
- DigitalCommons - The University of Maine Research (1)
- DigitalCommons@The Texas Medical Center (9)
- DigitalCommons@University of Nebraska - Lincoln (1)
- Diposit Digital de la UB - Universidade de Barcelona (1)
- DRUM (Digital Repository at the University of Maryland) (1)
- Duke University (1)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (7)
- FUNDAJ - Fundação Joaquim Nabuco (1)
- Helda - Digital Repository of University of Helsinki (6)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (1)
- Indian Institute of Science - Bangalore - Índia (52)
- Institutional Repository of Leibniz University Hannover (1)
- Instituto Nacional de Saúde de Portugal (1)
- Massachusetts Institute of Technology (1)
- National Center for Biotechnology Information - NCBI (13)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (6)
- Portal de Revistas Científicas Complutenses - Espanha (1)
- Publishing Network for Geoscientific & Environmental Data (36)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (13)
- Queensland University of Technology - ePrints Archive (441)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (1)
- Repositório Científico da Universidade de Évora - Portugal (2)
- Repositorio de la Universidad de Cuenca (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (14)
- Repositório Institucional da Universidade de Aveiro - Portugal (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (70)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (2)
- South Carolina State Documents Depository (1)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (10)
- Universidade Complutense de Madrid (1)
- Universidade de Lisboa - Repositório Aberto (1)
- Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP) (2)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (2)
- Université de Lausanne, Switzerland (1)
- Université de Montréal, Canada (2)
- Université Laval Mémoires et thèses électroniques (1)
- University of Queensland eSpace - Australia (13)
- University of Washington (3)
Resumo:
This paper demonstrates an optimal control solution to change of machine set-up scheduling based on dynamic programming average cost per stage value iteration as set forth by Cararnanis et. al. [2] for the 2D case. The difficulty with the optimal approach lies in the explosive computational growth of the resulting solution. A method of reducing the computational complexity is developed using ideas from biology and neural networks. A real time controller is described that uses a linear-log representation of state space with neural networks employed to fit cost surfaces.