Dynamic process modeling with recurrent neural networks
- Texas A and M Univ., College Station, TX (United States). Dept. of Chemical Engineering
Mathematical models play an important role in control system synthesis. However, due to the inherent nonlinearity, complexity and uncertainty of chemical processes, it is usually difficult to obtain an accurate model for a chemical engineering system. A method of nonlinear static and dynamic process modeling via recurrent neural networks (RNNs) is studied. An RNN model is a set of coupled nonlinear ordinary differential equations in continuous time domain with nonlinear dynamic node characteristics as well as both feed forward and feedback connections. For such networks, each physical input to a system corresponds to exactly one input to the network. The system's dynamics are captured by the internal structure of the network. The structure of RNN models may be more natural and attractive than that of feed forward neural network models, but computation time for training is longer. Simulation results show that RNNs can learn both steady-state relationships and process dynamics of continuous and batch, single-input/single-output and multi-input/multi-output systems in a simple and direct manner. Training of RNNs shows only small degradation in the presence of noise in the training data. Thus, RNNs constitute a feasible alternative to layered feed forward back propagation neural networks in steady-state and dynamic process modeling and model-based control.
- OSTI ID:
- 5996396
- Journal Information:
- AIChE Journal (American Institute of Chemical Engineers); (United States), Vol. 39:10; ISSN 0001-1541
- Country of Publication:
- United States
- Language:
- English
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99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
CHEMICAL REACTORS
NEURAL NETWORKS
PROCESS CONTROL
DIFFERENTIAL EQUATIONS
MATHEMATICAL MODELS
SYSTEMS ANALYSIS
TRAINING
CONTROL
EDUCATION
EQUATIONS
420200* - Engineering- Facilities
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990200 - Mathematics & Computers