Title

Resonating Perceptron Networks

Presenter Information

Niklas Melton

Department

Mechanical and Aerospace Engineering

Major

Aerospace Engineering

Research Advisor

Wunsch, Donald C.

Advisor's Department

Electrical and Computer Engineering

Funding Source

Missouri S&T OURE Program

Abstract

A novel learning algorithm was developed to increase the speed, stability, and robustness of neural function approximators. A clustering technique is used to identify corresponding regions of the input and output space. These regions and their mappings are then learned independently of all other regions. The resulting behavior is an algorithm which overcomes the problem of catastrophic forgetting while both increasing the speed of learning and minimizing the computational requirements of the network.

Biography

Niklas Melton was born and raised in Kansas City, MO. He will graduate from Missouri S&T this May with a B.S. in Aerospace Engineering. He has been accepted into the Ph.D. program in the Computer Engineering Department beginning in the Fall of 2017. He hopes to continue his fruitful research in neural networks and machine learning far into the future.

Research Category

Engineering

Presentation Type

Poster Presentation

Document Type

Poster

Location

Upper Atrium/Hall

Start Date

4-11-2017 1:00 PM

End Date

4-11-2017 3:00 PM

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Apr 11th, 1:00 PM Apr 11th, 3:00 PM

Resonating Perceptron Networks

Upper Atrium/Hall

A novel learning algorithm was developed to increase the speed, stability, and robustness of neural function approximators. A clustering technique is used to identify corresponding regions of the input and output space. These regions and their mappings are then learned independently of all other regions. The resulting behavior is an algorithm which overcomes the problem of catastrophic forgetting while both increasing the speed of learning and minimizing the computational requirements of the network.