1. Incremental Learning in Artificial Neural Networks
Project Name: may0114
ANNs, which function like clusters of neurons in a brain, are used to interpolate nonlinear systems that are too complex for traditional statistical analysis. By imitating the human brain, ANNs combine the versatility of humans with the speed of machines.
Figure 1: Diagram of a theoretical artificial neural network
Unlike humans, who incrementally learn information as it is introduced, ANNs learn all at once. An existing ANN cannot adapt to dynamically changing system. Hence, as a system changes, a new ANN must be created from scratch. For many real-life applications of ANNs, it is impractical to regularly create replacement ANNs.
The goal of this project is to create a more adaptive ANN, which learns incrementally, like humans. The new learning ANN will not suffer from the incremental learning problem that affects traditional ANNs.
The ANN must be coded using C++
The design must be object oriented
There must be a GUI to simplify the ANN’s use
The ANN will read a standardized data file format
Create a software design document
Create a GUI interface
Create an incrementally learning ANN
Apply to a power load problem and compare to traditional ANNs
Figure 2: Projected results of the end product
The end product is an incrementally learning ANN that will model dynamic nonlinear systems. It will include goodness measures to compare performance with traditional ANNs. Finally, users will operate the ANN through a user-friendly graphical interface.
What Are Artificial Neural Networks(ANN)?
Design Constraints
Milestones
Major Problem With ANNs
End Product Description
Project Goal
Team Members Project Name Advisor/Client
Herrick, David
Kerhin, Brian
Kirk, Christopher
Sharma, Ayush
May0114
Dr. Eric Bartlett (306 Town Engineering)
Dr. John Lamont
Special Thanks Goes to Dr. Eric Bartlett for his time and patience,
and Iowa State University for computational resources.