Artificial Neural Networks-Based Forecasting: An Attractive Option for Just-in-Time Systems
DOI10.1007/978-1-4614-1123-9_11zbMath1357.90042OpenAlexW82394310MaRDI QIDQ2958677
María Angélica Salazar-Aguilar, Maria G. Villarreal-Marroquin, Ángela Patricia Anaya Salazar, Mauricio Cabrera-Ríos
Publication date: 3 February 2017
Published in: Just-in-Time Systems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-1-4614-1123-9_11
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Management decision making, including multiple objectives (90B50) Production models (90B30)
Cites Work
- Nonlinear multiobjective optimization. A generalized homotopy approach
- Multilayer feedforward networks are universal approximators
- The accuracy of a procedural approach to specifying feedforward neural networks for fore\-casting
- Forecasting and recombining time-series components by using neural networks
- A simulation study of artificial neural networks for nonlinear time-series forecasting
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