Neural network based on adaptive chaotic gradient descending optimization algorithm and its application in matte converting process^①

来源期刊：中南大学学报(英文版)2004年第2期

论文作者：胡志坤 彭小奇 桂卫华

文章页码：216 - 219

Key words：matte converting; chaotic search; gradient descending; neural network

Abstract: An adaptive chaotic gradient descending optimization algorithm for single objective optimization was present-ed. A local minimum judged by two rules was obtained by an improved mutative-step gradient descending method. A new optimal minimum was obtained to replace the local minimum by mutative-scale chaotic search algorithm whose scales are magnified gradually from a small scale in order to escape local minima. The global optimal value was attained by repeatedly iterating. At last, a BP (back-propagation) neural network model for forecasting slag output in matte converting was es-tablished. The algorithm was used to train the weights of the BP neural network model. The simulation results with a training data set of 400 samples show that the training process can be finished within 300 steps to obtain the global optimal value, and escape local minima effectively. An optimization system for operation parameters, which includes the forecast-ing model, is achieved, in which the output of converter increases by 6.0%, and the amount of the treated cool materials rises by 7.8% in the matte converting process.