Expert system for controlling sinter chemistry based on neural network prediction
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摘要: 采用帶動量項的線性再勵自適應變步長BP神經網絡算法,建立了基于多周期運行模式的燒結礦化學成分預報模型;使用基于數據庫技術的知識庫和正向推理的推理機,開發了化學成分控制專家系統.系統自投入運行以來,預報模型命中率穩定在90%以上,操作指導建議采納率達到92%,實現了對燒結礦化學成分的穩定控制.Abstract: A sintering predictive model of chemical composition based on many periods was developed by the BP neural network algorithm with appending momentum and adaptive variable step size linear reinforcement. Using knowledge base that was based on database technology and illation with forward inference, an expert system was designed for controlling sinter chemistry. Since the system was plunged into application, the hit ratio of the predictive model is over 90% steadily, and the acceptance of operation suggestion is 92%. The goal of controlling chemical composition steadily is actualized.
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Key words:
- sinter /
- chemical composition /
- BP model /
- knowledge base /
- expert system
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