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Soft Sensor Modeling Based on Extreme Learning Machine and Case-Based Reasoning

机译：基于极限学习机和案例推理的软传感器建模

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摘要

Neural network (NN) and Case-based reasoning (CBR) have common advantages over other learning strategies. NN and CBR can be directly applied to the classification and regression problem without additional transform mechanisms. However, they all have disadvantages. The knowledge representation of NN is unreadable and this black box property restricts the application of NN to areas which needs proper explanations. Meanwhile CBR suffers from the feature-weighting problem, when CBR measures the distance between cases, some input features should be treated more importantly than others. This paper, we propose a hybrid prediction system of extreme learning machine (ELM) and Case-based reasoning (ELM-CBR). In our hybrid system, the feature weight set calculated from the trained ELM network plays the core role in connecting both the learning strategies, and the explanation on prediction can be given by presenting the most similar cases from the case base. Moreover, the prediction value of the Online Sequential Extreme Learning Machine also utilized in conjunction with the neighborhood information. This provides extended information for the query with most similar cases in the database. Finally, we present an application in the sugarcane juice clarification, experiments show that the hybrid system has a better recognition rate compared the k-NN and GA-CBR method.
机译：与其他学习策略相比，神经网络（NN）和基于案例的推理（CBR）具有共同的优势。 NN和CBR可以直接应用于分类和回归问题，而无需其他转换机制。但是，它们都有缺点。 NN的知识表示形式不可读，并且此黑匣子属性将NN的应用限制在需要适当说明的区域。同时，CBR存在特征加权问题，当CBR度量案例之间的距离时，某些输入特征应比其他特征更重要。本文提出了一种极限学习机（ELM）和基于案例的推理（ELM-CBR）的混合预测系统。在我们的混合系统中，从受过训练的ELM网络计算出的特征权重集在连接这两种学习策略中起着核心作用，并且可以通过从案例库中展示最相似的案例来给出预测的解释。此外，在线顺序极限学习机的预测值也与邻域信息结合使用。这为查询提供了数据库中最相似情况的扩展信息。最后，我们提出了一种在甘蔗汁澄清中的应用，实验表明，与k-NN和GA-CBR方法相比，该混合系统具有更好的识别率。

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