Non-linear Variable Structure Regression (VSR) and its application in time-series forecasting

Korjani, Mohammad M.; Mendel, Jerry M.

doi:10.1109/fuzz-ieee.2014.6891546

Cited by 7 publications

(3 citation statements)

References 45 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although not shown here, this difference increases when more terms per variable are used, e.g., using three terms per variables the candidate causal combinations for the concrete slump test data set is 134,217,728 whereas the number of surviving causal combinations is only 97 [19].…”

Section: Proof Letmentioning

confidence: 96%

“…In Korjani and Mendel [19] have shown how the surviving causal combinations can be used in a new regression model, called variable structure regression (VSR). Using the surviving causal combinations one can simultaneously determine the number of terms in the (nonlinear) regression model as well as the exact mathematical structure for each of the terms (basis functions).…”

Section: Discussionmentioning

confidence: 99%

“…A novel theoretical result leads to an exponential speedup in establishing this. For specific applications, see [7,8,19].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

On a Novel Way of Processing Data that Uses Fuzzy Sets for Later Use in Rule-Based Regression and Pattern Classification

Mendel¹

2014

International Journal of Fuzzy Logic and Intelligent Systems

Self Cite

View full text Add to dashboard Cite

This paper presents a novel method for simultaneously and automatically choosing the nonlinear structures of regressors or discriminant functions, as well as the number of terms to include in a rule-based regression model or pattern classifier. Variables are first partitioned into subsets each of which has a linguistic term (called a causal condition) associated with it; fuzzy sets are used to model the terms. Candidate interconnections (causal combinations) of either a term or its complement are formed, where the connecting word is AND which is modeled using the minimum operation. The data establishes which of the candidate causal combinations survive. A novel theoretical result leads to an exponential speedup in establishing this.

show abstract

Section: Proof Letmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

On a Novel Way of Processing Data that Uses Fuzzy Sets for Later Use in Rule-Based Regression and Pattern Classification

Mendel¹

2014

International Journal of Fuzzy Logic and Intelligent Systems

Self Cite

View full text Add to dashboard Cite

show abstract

On establishing nonlinear combinations of variables from small to big data for use in later processing

Mendel

Korjani

2014

Information Sciences

View full text Add to dashboard Cite

A Predictive Model for Improving the Efficiency of Frac Jobs

2015

View full text Add to dashboard Cite

This paper presents a new method for forecasting post-fracturing responses in a tight oil reservoir using historical hydraulic fracturing data. The methodology is based on a nonlinear regression method called Variable Structure Regression (VSR). Data for 80 frac jobs were used to calibrate and predict responses. Information per fracturing job included feet of perforation, number of perforations per stage, number of stages, pad volume, slurry volume and sand volume. Information for a set of hydraulically fractured vertical wells was used to test the proposed technique. The VSR method establishes the optimal nonlinear dependencies of variables to predict the cumulative liquid production after fracturing. This method determines the relationships between fracturing parameters and post-fracturing productions in the form of linguistic terms, thereby providing a physical understanding of the process. The exact mathematical structure of these linguistic terms and the number of terms are established simultaneously, thereby freeing the end user from time-consuming trial and error studies. Meanwhile, the knowledge of domain experts can be preserved as qualitative expert knowledge and may be combined with quantitative data. A Monte Carlo simulation of 5-fold cross-validation method on fracture optimization data set shows the practical accuracy of the VSR model on forecasting post-fracturing performance. The VSR method achieved satisfactory prediction accuracy as well as statistical performance given the relatively small training data size and the complexity of the problem. The final result shows increase prediction accuracy for all the wells that outperforms other existing methods.

show abstract

Non-linear Variable Structure Regression (VSR) and its application in time-series forecasting

Cited by 7 publications

References 45 publications

On a Novel Way of Processing Data that Uses Fuzzy Sets for Later Use in Rule-Based Regression and Pattern Classification

On a Novel Way of Processing Data that Uses Fuzzy Sets for Later Use in Rule-Based Regression and Pattern Classification

On establishing nonlinear combinations of variables from small to big data for use in later processing

A Predictive Model for Improving the Efficiency of Frac Jobs

Contact Info

Product

Resources

About