Explaining Black-Box Classifiers with ILP – Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules

2018 ◽  
pp. 105-117 ◽  
Author(s):  
Johannes Rabold ◽  
Michael Siebers ◽  
Ute Schmid
Keyword(s):  
Black Box ◽  
Non Linear ◽  
Relational Rules

Identification of semi-physical and black-box non-linear models: the case of MR-dampers for vehicles control☆

Automatica ◽  
2005 ◽  
Vol 41 (1) ◽  
pp. 113-127 ◽  
Author(s):  
S SAVARESI ◽  
S BITTANTI ◽  
M MONTIGLIO
Keyword(s):  
Linear Models ◽  
Black Box ◽  
Mr Dampers ◽  
Non Linear

Identification of semi-physical and black-box non-linear models: the case of MR-dampers for vehicles control

Automatica ◽  
2005 ◽  
Vol 41 (1) ◽  
pp. 113-127 ◽  
Author(s):  
Sergio M. Savaresi ◽  
Sergio Bittanti ◽  
Mauro Montiglio
Keyword(s):  
Linear Models ◽  
Black Box ◽  
Mr Dampers ◽  
Non Linear

scholarly journals Estimation of grey box and black box models for non-linear circuit data

2004 ◽  
Vol 37 (13) ◽  
pp. 399-404 ◽  
Author(s):  
Lennart Ljung ◽  
Qinghua Zhang ◽  
Peter Lindskog ◽  
Anatoli Juditski
Keyword(s):  
Black Box ◽  
Non Linear Circuit ◽  
Linear Circuit ◽  
Box Models ◽  
Non Linear ◽  
Black Box Models

scholarly journals Parameter identification of dc black-box arc model using non-linear least squares

2019 ◽  
Vol 2019 (16) ◽  
pp. 2202-2206 ◽  
Author(s):  
Kyu-Hoon Park ◽  
Ho-Yun Lee ◽  
Mansoor Asif ◽  
Bang-Wook Lee
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
Black Box ◽  
Linear Least Squares ◽  
Arc Model ◽  
Non Linear

scholarly journals The Application of Non-linear Cubic Regression in Rice Yield Predictions

2020 ◽  
Vol 3 (3) ◽  
pp. 227-234
Author(s):  
Retno Tri Vulandari ◽  
Hendro Wijayanto ◽  
Afan Lathofy
Keyword(s):  
Rice Yield ◽  
Unified Modeling Language ◽  
Black Box ◽  
Linear Regression Method ◽  
Validity Testing ◽  
Unified Modeling ◽  
Area Model ◽  
Non Linear ◽  
Black Box Testing ◽  
Rice Yields

The rice yields have fluctuated in Wonogiri Regency. This occasion happened in 2016-2018. Therefore, a prediction is needed to know whether rice yields will increase or decrease in the following year. The purpose of this study was to apply the polynomial non-linear regression method of third-degree in predicting rice yields. This study utilized the Unified Modeling Language (UML) as the system design, black-box testing as the functional testing, and MSE testing as the validity testing. The computed data was data of 2016-2018. The results showed that the prediction of 2017-2019 using the harvested area model produced more accurate calculations. The harvested area model produced the same MSE value in manual and application calculations, which were 405433,1349 in 2017, 312677,7798 in 2018, and 171183.6347 in 2019. The polynomial non-linear cubic regression is a solution to predict rice yields. The output of the application is the prediction information for rice yields

Simple black box models predicting potential control parameters during disturbances to a fluidised bed anaerobic reactor

1997 ◽  
Vol 36 (6-7) ◽  
pp. 229-237 ◽  
Author(s):  
G. C. Premier ◽  
R. Dinsdale ◽  
A. J. Guwy ◽  
F. R. Hawkes ◽  
D. L. Hawkes ◽  
...  
Keyword(s):  
Black Box ◽  
Gas Production ◽  
Operating Conditions ◽  
Organic Load ◽  
Fluidised Bed ◽  
Operating Life ◽  
Potential Control ◽  
Box Models ◽  
Non Linear ◽  
Black Box Models

Models of the anaerobic digestion process which predict digester behaviour sufficiently accurately could be used in process control. Although the process is generally considered to be non-linear, it could possibly be represented by an adaptive linear model, where the model adapts rapidly enough to represent the process at differing operating conditions and times in its operating life. Simple linear black box models of low order were investigated, predicting over a limited horizon and relying on current and recent data values to refine the prediction. Independent black box ARX models were identified for gas production rate, % CO2, bicarbonate alkalinity and Total Organic Carbon using on-line data from a fluidised bed reactor at varying organic load. Model predictions looked ahead one sample step (30 minutes) and when validated using data obtained in a different time period (separated by 4-8 weeks) gave significant predictions in each case. All the models consisted of only second or third order polynomials. The non-linear nature of the process was found to have little effect over the operating conditions investigated. Also the variation of the process within a 4-8 week period was not sufficient to cause the models to predict badly.

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