Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Naturally intelligent systems
Foundations of neural networks
Foundations of neural networks
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Introduction to artificial neural systems
Introduction to artificial neural systems
Advanced algorithms for neural networks: a C++ sourcebook
Advanced algorithms for neural networks: a C++ sourcebook
Understanding Neural Networks; Computer Explorations
Understanding Neural Networks; Computer Explorations
RBF Neural Network for Thrust and Torque Predictions in Drilling Operations
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
Optimum Back Propagation Network Conditions With Respect To Computation Time and Output Accuracy
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
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While there exists a broad range of neural networks for a particular task, different neural network architectures are selected depending upon the nature of application in industry. The range of applications covers anything from performance estimation and pattern recognition to process modelling and control. The network selection can be carried out based on economic considerations, such as cost associated with neural network computation time and obtaining data for required model variables. While each of the selected models can be a possible solution, depending upon the performance criteria, they all can be ranked from most suitable to least suitable for a particular application. In this paper, appraisal of neural networks for three industrial applications, involving process modelling of reduction cells for aluminium production, is discussed. Regression analysis techniques and six neural network models are assessed for their performance, using specific assessment criteria. It is shown that there is no single model that is most appropriate for each of the assessment criteria considered in each instance, hence, the decision of which neural network model is most suitable for a specific application is complex, particularly as the assessment criteria are not fundamentally of equal significance. It is shown that optimisation techniques are necessary to select an appropriate model for an application.