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Servo Data Set
Download: Data Folder, Data Set Description

Abstract: Data was from a simulation of a servo system

Data Set Characteristics:  

Multivariate

Number of Instances:

167

Area:

Computer

Attribute Characteristics:

Categorical, Integer

Number of Attributes:

4

Date Donated

1993-05-01

Associated Tasks:

Regression

Missing Values?

No

Number of Web Hits:

56580


Source:

Creator:

Karl Ulrich (MIT)

Donor:

Ross Quinlan


Data Set Information:

Ross Quinlan:

This data was given to me by Karl Ulrich at MIT in 1986. I didn't record his description at the time, but here's his subsequent (1992) recollection:

"I seem to remember that the data was from a simulation of a servo system involving a servo amplifier, a motor, a lead screw/nut, and a sliding carriage of some sort. It may have been on of the translational axes of a robot on the 9th floor of the AI lab. In any case, the output value is almost certainly a rise time, or the time required for the system to respond to a step change in a position set point."

(Quinlan, ML'93)

"This is an interesting collection of data provided by Karl Ulrich. It covers an extremely non-linear phenomenon - predicting the rise time of a servomechanism in terms of two (continuous) gain settings and two (discrete) choices of mechanical linkages."


Attribute Information:

1. motor: A,B,C,D,E
2. screw: A,B,C,D,E
3. pgain: 3,4,5,6
4. vgain: 1,2,3,4,5
5. class: 0.13 to 7.10


Relevant Papers:

Quinlan, J.R., "Learning with continuous classes", Proc. 5th Australian Joint Conference on AI (eds A. Adams and L. Sterling), Singapore: World Scientific, 1992
[Web Link]

Quinlan, J.R., "Combining instance-based and model-based learning", Proc. ML'93 (ed P.E. Utgoff), San Mateo: Morgan Kaufmann 1993
[Web Link]


Papers That Cite This Data Set1:

H. Altay Guvenir and Ilhan Uysal. Regression on feature projections. a Department of Computer Engineering, Bilkent University. 1999. [View Context].

Art B. Owen. Tubular neighbors for regression and classification. Stanford University. 1999. [View Context].

Christopher J. Merz and Michael J. Pazzani. A Principal Components Approach to Combining Regression Estimates. Machine Learning, 36. 1999. [View Context].

Mauro Birattari and Gianluca Bontempi and Hugues Bersini. Lazy Learning Meets the Recursive Least Squares Algorithm. NIPS. 1998. [View Context].

D. Greig and Hava T. Siegelmann and Michael Zibulevsky. A New Class of Sigmoid Activation Functions That Don't Saturate. 1997. [View Context].

Georg Thimm and E. Fiesler. Optimal Setting of Weights, Learning Rate, and Gain. E S E A R C H R E P R O R T I D I A P. 1997. [View Context].

Georg Thimm and Emile Fiesler. IDIAP Technical report High Order and Multilayer Perceptron Initialization. IEEE Transactions. 1994. [View Context].

Jianping Wu and Zhi-Hua Zhou and Cheng-The Chen. Ensemble of GA based Selective Neural Network Ensembles. National Laboratory for Novel Software Technology Nanjing University. [View Context].

Dorian Suc and Ivan Bratko. Combining Learning Constraints and Numerical Regression. National ICT Australia, Sydney Laboratory at UNSW. [View Context].

Georg Thimm and Emile Fiesler. High Order and Multilayer Perceptron Initialization. [View Context].


Citation Request:

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[1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info

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