Nonlinear Process Identification using Neural Networks (original) (raw)
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Nonlinear Identification of Process Dynamics Using Neural Networks
Nuclear Technology, 1992
The nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input/output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of a representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios.
IETE Journal of Research, 2007
This paper addresses the identification and control of nonlinear systems by means of Fuzzy Hammerstein (FH) models, which consist of a static fuzzy model connected in series with a linear dynamic model. For the identification of nonlinear dynamic systems with the proposed FH models, two methods are proposed. The first one is an alternating optimization algorithm that iteratively refines the estimate of the linear dynamics and the parameters of the static fuzzy model. The second method estimates the parameters of the nonlinear static model and of the linear dynamic model simultaneously by using a constrained recursive least-squares algorithm. The obtained FH model is incorporated in a model-based predictive control scheme and a new constraint-handling method is presented. A simulated water-heater process is used as an illustrative example. A comparison with an affine neural network and a linear model is given. Simulation results show that the proposed FH modeling approach is useful for modular parsimonious modeling and model-based control of nonlinear systems.
Review of identification techniques for nonlinear systems using neural networks
Control Engineering Practice, 1993
Most practical systems exhibit nonlinear characteristics of varying extent e.g from a simple valve to that of pH-processes in a chemical industry. In this paper we briefly review some of the Neural Network methods with a view to identify such systems. This is due to the fact that identification of the unknown system parameters form the backbone of most adaptive control strategies. In this paper the neural net techniques are reviewed from the point of view of approximating the hypersurface which maps the Input-Output space.
Identification of nonlinear dynamical systems using multilayered neural networks
Automatica, 1996
This paper discusses three learning algorithms to train R.ecrirrenl, Neural Networks for identification of non-linear dynamical systems. We select Memory Neural Networks(MNN) topology for the recurrent network in our work. MNNs are themselves dynamical systems t,hat have internal memory ohtained by adding trainalile temporal eleinerits to feed-forward networks. Three leariling procedures nemcly Back-Propagation Through Tiriie(BPTT), Real Time Recurrent Learning(RTRL) and Ext,ended Kalman Filtering(EKF) are used for adjusting the weights in &INN to train such networks to identify the plant. The relative effectiveness of different learning algorithms have been discussed by comparing the mean square error associated with them and corresponding computut,ional requirements. T h e simulation results show-that HTRL algorithm is efficient for trainiug MNNs to model noiilinear dynamical systems by considering both compntat.ional complexity and modelling accnracy. Eventhough. the accuracy of system identification is best with EKF, but it has the drawback of heing computationally int,ensive.
System identification of non-linear process system is important and beneficial in the process industries. The main objective for the modelling task is to obtain a good and reliable tool for analysis and control system development. In this work, a model identification of a nonlinear process is performed by Nonlinear Autoregressive exogenous (NARX) Recurrent Neural Network and Elman Recurrent Neural Network (ERNN) approach. Developed models performance are analysed and best among them can be used in off-line controller design and implementation of new advanced control schemes. Here, a challenging nonlinear process level control in the gravity discharge tank taken into an account, since its nonlinearity and constantly changing of cross section with respect to rise in liquid level. The model for such non-linear process were to be identified for different operating regions and are approximated to first order plus dead time model. The developed models performance is analysed for different regions and best among the two approaches, one is highlighted.
… Distributed and Grid …, 2012
The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two target applications i.e. nuclear reactor power level monitoring and an AC servo position control system. Various configurations of ANN using different activation functions, number of hidden layers and neurons in each layer are trained and tested to find out the best configuration. The training is carried out multiple times to check for consistency and the mean and standard deviation of the root mean square errors (RMSE) are reported for each configuration.
IEEE/CAA Journal of Automatica Sinica, 2022
This letter presents a practical industrial process identification scheme. More specifically, to improve the identification accuracy of practical process, a decoupled identification scheme is developed based on neural fuzzy network and autoregressive exogenous (ARX) model, which is based on multi-signal sources. The multiple signal sources include binary signals and random signals. Experimental results of pH neutralization process show that developed identification scheme can provide accurate identification accuracy.
Neural Network Structures for On-Line Identification and Optimization of Nonlinear Processes
This paper analyzes various formulations for the recursive training of neural networks that can be used for identifying and optimizing nonlinear processes on line. The study considers feedforward type networks (FFNN) adapted by three different methods. The study is completed using two network structures that are linear in the parameters: a radial basis network (RBF) and a principal components (PCA) network, both trained using a recursive least squares algorithm. The corresponding algorithms and a comparative test consisting of the on-line estimation of a reaction rate are detailed. The results indicate that all the structures were capable of converging satisfactorily in a few iteration cycles, FFNN type networks showing better prediction capacity than linear parameter networks, but the computational effort of the recursive algorithms is greater.
Nonlinear System Identification Using Neural Network
Springer-Verlag Berlin Heidelberg, 2012
Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.
Nonlinear System Identification of Laboratory Heat Exchanger Using Artificial Neural Network Model
International Journal of Electrical and Computer Engineering, 2013
This paper addresses the nonlinear identification of liquid saturated steam heat exchanger (LSSHE) using artificial neural network model. Heat exchanger is a highly nonlinear and non-minimum phase process and often its working conditions are variable. Experimental data obtained from fluid outlet temperature measurement in laboratory environment is used as the output variable and the rate of change of fluid flow into the system as input too. The results of identification using neural network and conventional nonlinear models are compared together. The simulation results show that neural network model is more accurate and faster in comparison with conventional nonlinear models for a time series data because of the independence of the model assignment.