Genetic Programming for the Identification of Nonlinear Input-Output Models

Linear-in-parameters models are quite widespread in process engineering, e.g. NAARX, polynomial ARMA models, etc. This paper proposes a new method for structure selection of these models

Software Sensor for Activity-Time Monitoring and Fault Detection in Production Lines

Industry 4.0-based human-in-the-loop cyber-physical production systems are transforming the industrial workforce to accommodate the ever-increasing variability of production. Real-time operator support and performance monitoring require accurate information on the activities of operators. The problem with tracing hundreds of activity times is critical due to the enormous variability and complexity of products. To handle this problem a software-sensor-based activity-time and performance measurement system is proposed. To ensure a real-time connection between operator performance and varying product complexity, fixture sensors and an indoor positioning system (IPS) were designed and this multi sensor data merged with product-relevant information. The proposed model-based performance monitoring system tracks the recursively estimated parameters of the activity-time estimation model. As the estimation problem can be ill-conditioned and poor raw sensor data can result in unrealistic parameter estimates, constraints were introduced into the parameter-estimation algorithm to increase the robustness of the software sensor. The applicability of the proposed methodology is demonstrated on a well-documented benchmark problem of a wire harness manufacturing process. The fully reproducible and realistic simulation study confirms that the indoor positioning system-based integration of primary sensor signals and product-relevant information can be efficiently utilized in terms of the constrained recursive estimation of the operator activity

Interactive Evolutionary Computation in Process Engineering

In practical system identification, process optimization and controller design, it is often desirable to simultaneously handle several objectives and constraints. In some cases, these objectives and constraints are non-commensurable and they are not explicitly/mathematically available. This paper proposes a new subjective optimization method based on Interactive Evolutionary Computation (IEC) to handle these problems. IEC is an evolutionary algorithm whose fitness function is provided by human users. The whole approach has been implemented in MATLAB (EAsy-IEC Toolbox) and applied to two case-studies: tuning a Model Predictive Controller and temperature profile design of a batch beer fermenter. The results show that IEC is an e#cient and comfortable method to incorporate the prior knowledge of the user into optimization problems. The developed EASyIEC Toolbox (for Matlab) can be downloaded from the website of the authors: http://www.fmt.vein.hu/softcomp/EAsy

Feedback Linearizing Control Using Hybrid Neural Networks Identified . . .

Globally Linearizing Control (GLC) is a control algorithm capable of using nonlinear process model directly. In GLC, mostly, first-principles models derived from dynamic mass, energy and momentum balances are used. When the process is not perfectly known, the unknown parts of the first principles model should be represented by black-box models, e.g. by neural networks. This paper is devoted to the identification and application of such hybrid models for GLC. It is shown that the first principles part of the model determines the dominant structure of the controller, while the black-box elements of the hybrid model are used as state and/or disturbance estimators. For the identification of the neural network elements of the hybrid model a sensitivity approach based algorithm has been developed. The underlying framework is illustrated by the temperature control of a continuous stirred tank reactor (CSTR) where a neural network is used to model the heat released by an exothermic chemical reaction

Graph configuration model based evaluation of the education-occupation match.

To study education-occupation matchings we developed a bipartite network model of education to work transition and a graph configuration model based metric. We studied the career paths of 15 thousand Hungarian students based on the integrated database of the National Tax Administration, the National Health Insurance Fund, and the higher education information system of the Hungarian Government. A brief analysis of gender pay gap and the spatial distribution of over-education is presented to demonstrate the background of the research and the resulted open dataset. We highlighted the hierarchical and clustered structure of the career paths based on the multi-resolution analysis of the graph modularity. The results of the cluster analysis can support policymakers to fine-tune the fragmented program structure of higher education

Structure Identification of Fuzzy Classifiers

For complex and high-dimensional problems, data-driven identification of classifiers has to deal with structural issues like the selection of the relevant features and effective initial partition of the input domain. Therefore, the identification of fuzzy classifiers is a challenging topic. Decision-tree (DT) generation algorithms are effective in feature selection and extraction of crisp classification rules, hence they can be used for the initialization of fuzzy systems. Because fuzzy classifiers have much flexible decision boundaries than DTs, fuzzy models can be more parsimonious than DTs. Hence, to get compact, easily interpretable and transparent classification system, a new structure identification algorithm is proposed, where genetic algorithm (GA) based parameter optimization of the DT initialized fuzzy sets is combined with similarity based rule base simplification algorithms. The performance of the approach is studied on a specially designed artificial data. An application to the Cancer classification problem is also shown