Support Vector Identification of Seismic Electric Signals

Ifantis, Apostolos; Papadimitriou, Stergios

doi:10.1142/s0218001403002484

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The overfitting risk is low due to the robust mathematical design. The required computational power is also small due to efficient algorithms [ 25 , 26 ]. Extremely complex nonlinear input-output relationships can be handled with relative ease, and unique solutions can be found even with large data sets [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…The required computational power is also small due to efficient algorithms [ 25 , 26 ]. Extremely complex nonlinear input-output relationships can be handled with relative ease, and unique solutions can be found even with large data sets [ 26 ]. In addition, the risk of misclassification is minimized by maximizing the margin as outliers can be detected.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the risk of misclassification is minimized by maximizing the margin as outliers can be detected. The empirical training error is minimized [ 24 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Sand-Binder Mixtures from the Foundry Industry Using Electrical Impedance Spectroscopy and Support Vector Machines

Bifano,

Ma,

Fischerauer

2024

Sensors

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Molding sand mixtures used in the foundry industry consist of various sands (quartz sands, chromite sands, etc.) and additives such as bentonite. The optimum control of the processes involved in using the mixtures and in their regeneration after the casting requires an efficient in-line monitoring method that is not available today. We are investigating whether such a method can be based on electrical impedance spectroscopy (EIS). To establish a database, we have characterized various sand mixtures by EIS in the frequency range from 0.5 kHz to 1 MHz under laboratory conditions. Attempts at classifying the different molding sand mixtures by support vector machines (SVM) show encouraging results. Already high assignment accuracies (above 90%) could even be improved with suitable feature selection (sequential feature selection). At the same time, the standard uncertainty of the SVM results is low, i.e., data assigned to a class by the presented SVMs have a high probability of being assigned correctly. The application of EIS with subsequent evaluation by machine learning (machine-learning-enhanced EIS, MLEIS) in the field of bulk material monitoring in the foundry industry appears possible.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Sand-Binder Mixtures from the Foundry Industry Using Electrical Impedance Spectroscopy and Support Vector Machines

Bifano,

Ma,

Fischerauer

2024

Sensors

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The Model of Order Prediction Based on SVM

Hua

Yan

2007

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007)

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Efficient and interpretable fuzzy classifiers from data with support vector learning

Papadimitriou

Terzidis

2005

IDA

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The maximization of the performance of the most if not all the fuzzy identification techniques is usually expressed in terms of the generalization performance of the derived neuro-fuzzy construction. Support Vector algorithms are adapted for the identification of a Support Vector Fuzzy Inference (SVFI) system that obtains robust generalization performance. However, these SVFI rules usually lack of interpretability. The accurate set of rules can be approximated with a simpler interpretable fuzzy system that can present insight to the more important aspects of the data. The interpretable fuzzy system construction algorithms receive an a priori description of a set of fuzzy sets that describe the linguistic aspects of the input variables as they are usually perceived by the human experts. In the case of the interpretable fuzzy sets an adaptive an algorithm for building them automatically is presented here. After the construction of the interpretable fuzzy partitions, the developed algorithms extract from the SVFI rules a small and concise set of interpretable rules. Finally, the Pseudo-Outer Product (POP) fuzzy rule selection orders the interpretable rules by using a Hebbian like evaluation in order to present the designer with the most capable rules.

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Support Vector Identification of Seismic Electric Signals

Cited by 3 publications

References 16 publications

Classification of Sand-Binder Mixtures from the Foundry Industry Using Electrical Impedance Spectroscopy and Support Vector Machines

Classification of Sand-Binder Mixtures from the Foundry Industry Using Electrical Impedance Spectroscopy and Support Vector Machines

The Model of Order Prediction Based on SVM

Efficient and interpretable fuzzy classifiers from data with support vector learning

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