Recursive prediction for long term time series forecasting using advanced models

Data-driven adaptive modeling method for industrial processes and its application in flotation reagent control

In real industrial processes, new process "excitation" patterns that largely deviate from previously collected training data will appear due to disturbances caused by process inputs. To reduce model mismatch, it is important for a data-driven process model to adapt to new process "excitation" patterns. Although efforts have been devoted to developing adaptive process models to deal with this problem, few studies have attempted to develop an adaptive process model that can incrementally learn new process "excitation" patterns without performance degradation on old patterns. In this study, efforts are devoted to enabling data-driven process models with incremental learning ability. First, a novel incremental learning method is proposed for process model updating. Second, an adaptive neural network process model is developed based on the novel incremental learning method. Third, a nonlinear model predictive control based on the adaptive process model is implemented and applied for flotation reagent control. Experiments based on historical data provide evidence that the newly developed adaptive process model can accommodate new process "excitation" patterns and preserve its performance on old patterns. Furthermore, industry experiments carried out in a real-world lead-zinc froth flotation plant provide industrial evidence and show that the newly designed controller is promising for practical flotation reagent control.

Granular Model of Long-Term Prediction for Energy System in Steel Industry

Sound energy scheduling and allocation is of paramount significance for the current steel industry, and the quantitative prediction of energy media is being regarded as the prerequisite for such challenging tasks. In this paper, a long-term prediction for the energy flows is proposed by using a granular computing-based method that considers industrial-driven semantics and granulates the initial data based on the specificity of manufacturing processes. When forming information granules on a basis of experimental data, we propose to deal with the unequal-length temporal granules by exploiting dynamic time warping, which becomes instrumental to the realization of the prediction model. The model engages the fuzzy C -means clustering method. To quantify the performance of the proposed method, real-world industrial energy data coming from a steel plant in China are employed. The experimental results demonstrate that the proposed method is superior to some other data-driven methods and becomes capable of satisfying the requirements of the practically viable prediction.

Short-Term Prediction of Marine Sensor Data with Fuzzy Clustering

In predicting water quality variables in the short term, a novel technique using fuzzy pattern similarity-based fuzzy clustering has been proposed. The experimental results show that the proposed method outperforms than existing similar methods for sea water temperature and conductivity data sets from a marine sensor network for environmental monitoring. The short-term prediction of water quality variables has immense benefit in aquaculture and fisheries industries for decision-making purposes.

Genetic Fuzzy System Predicting Contractile Reactivity Patterns of Small Arteries

Monitoring of physiological surrogate end points in drug development generates dynamic time-domain data reflecting the state of the biological system. Conventional data analysis often reduces the information in these data by extracting specific data points, thereby discarding potentially useful information. We developed a genetic fuzzy system (GFS) algorithm that is capable of learning all information in time-domain physiological data. Data on isometric force development of isolated small arteries were used as a framework for developing and optimizing a GFS. GFS performance was improved by several strategies. Results show that optimized fuzzy systems (OFSs) predict contractile reactivity of arteries accurately. In addition, OFSs identified significant differences that were undetectable using conventional analysis in the responses of arteries between groups. We concluded that OFSs may be used in clustering or classification tasks as aids in the objective identification or prediction of dynamic physiological behavior.

Using machine learning techniques and genomic/proteomic information from known databases for defining relevant features for PPI classification

In modern proteomics, prediction of protein-protein interactions (PPIs) is a key research line, as these interactions take part in most essential biological processes. In this paper, a new approach is proposed to PPI data classification based on the extraction of genomic and proteomic information from well-known databases and the incorporation of semantic measures. This approach is carried out through the application of data mining techniques and provides very accurate models with high levels of sensitivity and specificity in the classification of PPIs. The well-known support vector machine paradigm is used to learn the models, which will also return a new confidence score which may help expert researchers to filter out and validate new external PPIs. One of the most-widely analyzed organisms, yeast, will be studied. We processed a very high-confidence dataset by extracting up to 26 specific features obtained from the chosen databases, half of them calculated using two new similarity measures proposed in this paper. Then, by applying a filter-wrapper algorithm for feature selection, we obtained a final set composed of the eight most relevant features for predicting PPIs, which was validated by a ROC analysis. The prediction capability of the support vector machine model using these eight features was tested through the evaluation of the predictions obtained in a set of external experimental, computational, and literature-collected datasets.