69,657 research outputs found

    Data-driven Soft Sensors in the Process Industry

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    In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

    Application of Computational Intelligence Techniques to Process Industry Problems

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    In the last two decades there has been a large progress in the computational intelligence research field. The fruits of the effort spent on the research in the discussed field are powerful techniques for pattern recognition, data mining, data modelling, etc. These techniques achieve high performance on traditional data sets like the UCI machine learning database. Unfortunately, this kind of data sources usually represent clean data without any problems like data outliers, missing values, feature co-linearity, etc. common to real-life industrial data. The presence of faulty data samples can have very harmful effects on the models, for example if presented during the training of the models, it can either cause sub-optimal performance of the trained model or in the worst case destroy the so far learnt knowledge of the model. For these reasons the application of present modelling techniques to industrial problems has developed into a research field on its own. Based on the discussion of the properties and issues of the data and the state-of-the-art modelling techniques in the process industry, in this paper a novel unified approach to the development of predictive models in the process industry is presented

    Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting

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    In lifelong learning systems, especially those based on artificial neural networks, one of the biggest obstacles is the severe inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting. In this article, we propose a new kind of connectionist architecture, the Sequential Neural Coding Network, that is robust to forgetting when learning from streams of data points and, unlike networks of today, does not learn via the immensely popular back-propagation of errors. Grounded in the neurocognitive theory of predictive processing, our model adapts its synapses in a biologically-plausible fashion, while another, complementary neural system rapidly learns to direct and control this cortex-like structure by mimicking the task-executive control functionality of the basal ganglia. In our experiments, we demonstrate that our self-organizing system experiences significantly less forgetting as compared to standard neural models and outperforms a wide swath of previously proposed methods even though it is trained across task datasets in a stream-like fashion. The promising performance of our complementary system on benchmarks, e.g., SplitMNIST, Split Fashion MNIST, and Split NotMNIST, offers evidence that by incorporating mechanisms prominent in real neuronal systems, such as competition, sparse activation patterns, and iterative input processing, a new possibility for tackling the grand challenge of lifelong machine learning opens up.Comment: Key updates including results on standard benchmarks, e.g., split mnist/fmnist/not-mnist. Task selection/basal ganglia model has been integrate
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