Neural network approximation of piecewise continuous functions: application to friction compensation

By Rastko R. Selmic, Frank L. Lewis. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sergey Levine
2. Peter R. Florence
3. Andy Zeng
4. Corey Lynch
5. Jonathan Tompson
6. Wei He
7. Yilun Du
8. Johnny Lee

Citations in the corpus

• Implicit Behavioral Cloning (2021, background): “Also motivated by applications to modeling phenomena for robots, [64] develops theory of approximating discontinuous functions with neural networks, but the method requires a-priori knowledge of the discontinuity’s location.”
• Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, background): “Therefore, a wide range of problems is solved by the FNNs, such as pattern recognition [3], clustering and classification [4], function approximation [5], control [6], bioinformatics [7], signal processing [8], speech processing [9], etc.”
• Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint (2016, background): “Nonlinearities are not analytical and it is quite difficult to know their exact models [1]–[3].”