Data-driven Soft Sensors in the Process Industry

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

Rough Fuzzy Subspace Clustering for Data with Missing Values

The paper presents rough fuzzy subspace clustering algorithm and experimental results of clustering. In this algorithm three approaches for handling missing values are used: marginalisation, imputation and rough sets. The algorithm also assigns weights to attributes in each cluster; this leads to subspace clustering. The parameters of clusters are elaborated in the iterative procedure based on minimising of criterion function. The crucial parameter of the proposed algorithm is the parameter having the influence on the sharpness of elaborated subspace cluster. The lower values of the parameter lead to selection of the most important attribute. The higher values create clusters in the global space, not in subspaces. The paper is accompanied by results of clustering of synthetic and real life data sets

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

HIV analysis using computational intelligence

In this study, a new method to analyze HIV using a combination of autoencoder networks and genetic algorithms is proposed. The proposed method is tested on a set of demographic properties of individuals obtained from the South African antenatal survey. The autoencoder model is then compared with a conventional feedforward neural network model and yields a classification accuracy of 92% compared to 84% obtained for the conventional feedforward model. The autoencoder model is then used to propose a new method of approximating missing entries in the HIV database using ant colony optimization. This method is able to estimate missing input to an accuracy of 80%. The estimated missing input values are then used to analyze HIV. The autoencoder network classifier model yields a classification accuracy of 81% in the presence of missing input values. The feedforward neural network classifier model yields a classification accuracy of 82% in the presence of missing input values. A control mechanism is proposed to assess the effect of demographic properties on the HIV status of individuals, based on inverse neural networks, and autoencoder networks-based-on-genetic algorithms. This control mechanism is aimed at understanding whether HIV susceptibility can be controlled by modifying some of the demographic properties. The inverse neural network control model has accuracies of 77% and 82%, meanwhile the genetic algorithm model has accuracies of 77% and 92%, for the prediction of educational level of individuals, and gravidity, respectively. HIV modelling using neuro-fuzzy models is then investigated, and rules are extracted, which provide more valuable insight. The classification accuracy obtained by the neuro-fuzzy model is 86%. A rough set approximation is then investigated for rule extraction, and it is found that the rules present simplistic and understandable relationships on how the demographic properties affect HIV risk. The study concludes by investigating a model for automatic relevance determination, to determine which of the demographic properties is important for HIV modelling. A comparison is done between using the full input data set and the data set using the input parameters selected by the technique for the HIV classification. Age of the individual, gravidity, province, region, reported pregnancy and educational level were amongst the input parameters selected as relevant for classification of an individual’s HIV risk. This study thus proposes models, which can be used to understand HIV dynamics, and can be used by policy-makers to more effectively understand the demographic influences driving HIV infection