A hybrid science‐guided machine learning approach for modeling chemical processes: A review
This study presents a broad perspective of hybrid process modeling combining the scientific
knowledge and data analytics in bioprocessing and chemical engineering with a science …
knowledge and data analytics in bioprocessing and chemical engineering with a science …
Advances in artificial neural networks–methodological development and application
Y Huang - Algorithms, 2009 - mdpi.com
Artificial neural networks as a major soft-computing technology have been extensively
studied and applied during the last three decades. Research on backpropagation training …
studied and applied during the last three decades. Research on backpropagation training …
Evaluation of deep learning models for multi-step ahead time series prediction
R Chandra, S Goyal, R Gupta - Ieee Access, 2021 - ieeexplore.ieee.org
Time series prediction with neural networks has been the focus of much research in the past
few decades. Given the recent deep learning revolution, there has been much attention in …
few decades. Given the recent deep learning revolution, there has been much attention in …
Learning long-term dependencies in NARX recurrent neural networks
It has previously been shown that gradient-descent learning algorithms for recurrent neural
networks can perform poorly on tasks that involve long-term dependencies, ie those …
networks can perform poorly on tasks that involve long-term dependencies, ie those …
Type-2 fuzzy logic: theory and applications
Type-2 fuzzy sets are used for modeling uncertainty and imprecision in a better way. These
type-2 fuzzy sets were originally presented by Zadeh in 1975 and are essentially" fuzzy …
type-2 fuzzy sets were originally presented by Zadeh in 1975 and are essentially" fuzzy …
Reinforcement learning for batch bioprocess optimization
Bioprocesses have received a lot of attention to produce clean and sustainable alternatives
to fossil-based materials. However, they are generally difficult to optimize due to their …
to fossil-based materials. However, they are generally difficult to optimize due to their …
Computational capabilities of recurrent NARX neural networks
HT Siegelmann, BG Horne… - IEEE Transactions on …, 1997 - ieeexplore.ieee.org
Recently, fully connected recurrent neural networks have been proven to be computationally
rich-at least as powerful as Turing machines. This work focuses on another network which is …
rich-at least as powerful as Turing machines. This work focuses on another network which is …
[图书][B] Nonlinear process control
MA Henson, DE Seborg - 1997 - cse.sc.edu
In the past decade, the control of nonlinear systems has received considerable attention in
both academia and industry. The recent interest in the design and analysis of nonlinear …
both academia and industry. The recent interest in the design and analysis of nonlinear …
Review of the applications of neural networks in chemical process control—simulation and online implementation
MA Hussain - Artificial intelligence in engineering, 1999 - Elsevier
As a result of good modeling capabilities, neural networks have been used extensively for a
number of chemical engineering applications such as sensor data analysis, fault detection …
number of chemical engineering applications such as sensor data analysis, fault detection …
[图书][B] A field guide to dynamical recurrent networks
Acquire the tools for understanding new architectures and algorithms of dynamical recurrent
networks (DRNs) from this valuable field guide, which documents recent forays into artificial …
networks (DRNs) from this valuable field guide, which documents recent forays into artificial …