Control of pH in-line using a neural predictive strategy

Control of an experimental in-line pH process exhibiting varying nonlinearity and deadtime is described. A radial basis function (RBF) artificial neural network is used to model the nonlinear dynamics of the process. Accommodation of the varying process deadtime in the neural model is achieved by the generation of a feed-forward signal, for input to the neural network, from a downstream pH measurement. The feedforward signal is derived from a variable delay model based on process knowledge and a flow measurement. The neural model is then used to realise a predictive control scheme for the process. Development of the neural process model is described and results are presented to illustrate the performance of the neural predictive control scheme which is tested as a regulator at different setpoints

Design issues in applying neural networks to model highly non-linear processes

his paper looks at the selection of some of the design parameters which are crucially important for the training of a valid artificial neural network (ANN) model of processes with strong nonlinearities. Arbitrary selection of data sample time and network structure can result in an ANN model with unacceptable prediction errors. Useful guidelines concerning data sample time and model structure can be obtained by studying local linear models. The Akaike's final prediction error (AFPE) and Akaike's information criterion (AIC) penalise overparameterised networks and are therefore useful indicators of model parsimony. They can be used in conjunction with correlation analysis for model selection and validation

Enhancing the non-linear modelling capabilities of MLP neural networks using spread encoding

Two methods for representing data in a multi-layer perceptron (MLP) neural network are described and the resultant ability of networks, trained by the standard back-propagation algorithm, to identify the dynamics of non-linear systems is investigated. One of the data conditioning methods has been widely used in studies of the MLP network and consists of normalising each network input and output variable and applying the normalised data to single network nodes. In the second method, named spread encoding, each network variable is represented as a sliding Gaussian pattern of excitations across several network nodes. The spread encoding technique exhibits similarities with conventional algorithms used in fuzzy logic and a network utilising this method can be considered as a fuzzy-neural type network. Neural networks are configured to represent a non-linear, auto-regressive, exogenous (NARX) input-output model structure and the performance of trained networks is investigated in applications to modelling a real liquid level process unit and a simulation of a highly non-linear chemical process. Results show that using the data normalisation method, a network can provide accurate single-step predictions but is incapable of adequate long-range predictions. In contrast to this, the spread encoding technique significantly enhances the performance of a MLP network model enabling accurate single-step and long-range predictions to be achieved

On-line detection of fault conditions in controlled industrial processes

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