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中文摘要：
针对运行工况频繁波动、单一模型难以描述过程特性的问题,提出了带有工况中心修正的多模型在线建模方案,包括工况识别机制、局部模型、多模型合成机制.工况识别机制根据工况特征变量分析工况范围,由相近度修正工况中心;局部模型采用Hammerstein模型,非线性增益由带有稳定学习算法的小波神经网络建立,线性模型由带控制量的自回归模型（ARX）建立;多模型合成机制采用加权求和方法.在线修正工况中心可反映工况的时间变化特性,参数稳定学习算法改善了模型精度和自适应能力.采用此方法建立污水处理过程化学需氧量（COD）软测量模型,结果表明,模型在工况大范围变化时仍具有满意预测效果.
英文摘要：
Because a single model cannot represent the characteristics of the complex industrial process in varying operating ranges, we propose an online modeling scheme for multiple models This scheme includes the recognition mechanism of operating range, the local models and the combination mechanism for multiple models. The recognition mechanism analyzes the operating range according to its characteristic variables and adjusts the center of operating range according to similarity degrees. The local model is actually a Hammerstein model which is the serial connection of a wavelet neural network with a stable learning algorithm and an ARX model. The combination mechanism calculates the weighted sum of the outputs of local models, and online adjusts the centers of operating range to reflect the variation characteristics of the operating range. A stable learning algorithm of parameters improves the prediction accuracy and the adaptation ability. This method is implemented in a wastewater treatment process to measure the concentration of the chemical oxygen demand （COD）. Experimental results show that this modeling scheme can obtain satisfactory effect in varvin~ ooerating ranges.
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