Identifying significant parameters for Hall-Heroult process using general regression neural networks
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
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Neural networks, due to their excellent capabilities for modeling process behavior, are gaining precedence over traditional empirical modeling techniques, such as statistical methods. While neural networks have good ability to map any reasonable continuous function, they do not explain easily how the inputs are related to an output, and also whether the selected inputs have any significant relationship with an output. There is quite often a need to identify some order of influence of the input variables on the output variable. In this paper, a technique for determining the order of influence of the n elements of the input vector on the m elements of the output vector is presented and discussed. While a sample mathematical function is used to introduce the technique, a more practical application of this method in the aluminium smelting industry is considered. It is shown that using a sensitivity analysis on the backpropagation algorithm the degree of influence of input parameters on the output error can be successfully estimated.