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This project aims to develop an incrementally learning artificial neural network (ANN) that can adapt to dynamic systems without catastrophic forgetting. The end product includes a GUI for interacting with the ANN and modeling nonlinear systems, such as power load forecasting and market trend prediction. The project focuses on creating a software design document, implementing incremental learning capabilities, and providing comparative analysis with traditional ANNs. The technical approach involves using augmenting neural nets and exploring different design alternatives to enhance learning efficiency. The evaluation of project success highlights meeting first-semester milestones and outlines recommendations for future work. Lessons learned emphasize effective team communication and time management strategies. This initiative opens up new possibilities in the field of artificial intelligence and neural network development.
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Introduction Incremental Learning With Neural Networks February 1, 2001 May01-14 Team Members David Herrick Brian Kerhin Chris Kirk Ayush Sharma Clients/Faculty Advisors Dr. Eric Bartlett
Overview Overview • Problem Statement (Ayush) • Design Objectives (Ayush) • Intended Users (Ayush) • End-Product Description (Brian) • Assumptions/Limitations (Brian) • Project Risks & Concerns (Brian) • Technical Approach (Brian) • System Overview (Dave) • Technical Design (Dave) • Evaluation of Project Success (Chris) • Possible Future Work (Chris) • Human/Financial Budget (Chris) • Lessons Learned (Chris) • Closing Summary (Chris)
ANN Description ANN Description = • Real neurons are decision making cells in the brain • Software neurons function similarly, to make decisions in software • The brain is a network of “Real neurons” making decisions in parallel • ANNs accomplish the same goal using “Software neurons” • ANNs are used to interpolate nonlinear systems that are very complex • Predict trends in the Stock Market • Predict trends in power use through out the year • Predict trends in global whether patterns
Problem Statement Problem Statement Unlike humans, who incrementally learn information as it is introduced, ANNs learn all at once. An existing ANN cannot adapt to dynamically changing system (i.e. CATASTROPHIC FORGETTING). 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.
Design Objectives Design Objectives • Create a software design document • Create an incrementally learning ANN • Create a GUI for incrementally learning ANN • Apply to a power load problem and compare to traditional ANNs
Operation and Construction Environment Hardware and Software (Supplies) • Hardware • Intel Gateway PCs in Adaptive Computing Laboratory • Software • Microsoft Windows NT4.0 • Microsoft Visual C++ • Microsoft Visual Basic
Intended Users Intended Users • Adaptive Computing Laboratory • Dr Eric Bartlett • Research Assistants • Other Neural Network Programmers
End-Product Description End-Product Description • Incrementally learning ANN to model dynamic nonlinear systems • Learning System can learn new data without forgetting • Has goodness measures • GUI operated, via command line
Assumptions Assumptions • User should have a basic knowledge of neural networks • Data file will be tab delimited following Dr Bartlett’s specified file format
Limitations Limitations • Set time period to produce code and documents • Number of developers is not based on commercial properties of the software • Computationally intensive technique of problem solving
Project Risks and Concerns Project Risks & Concerns • Major accidents can (and have) cripple team members • Finishing on time • Incremental learning may not improve goodness measures or error results
Technical Approach Technical Approach (Design Alternatives) • One strong neural network with augmenting neural nets • Several augmenting neural nets • Update a single augmenting neural net • Produce result based on outputs average • Produce result based on output sums • Multiple weak neural nets average results
System Overview Incremental Learning System
Technical Design Technical Design
Technical Design (cont.) Technical Design
System Overview Incremental Learning System
Evaluation of Project Success Evaluation of Project Success • 1st Semester Milestones • Project Plan (Fully Met) • Project Poster (Fully Met) • Design Report (Fully Met) • Hardware Requirements (Fully Met) • Software Requirements (Fully Met) • Software Design (Fully Met)
Evaluation of Project Success (cont.) Evaluation of Project Success (cont.) • 2nd Semester Milestones • Software Implementation (Partially Met) • 100% of algorithm designed • 10% of algorithm implemented • Final Implementation (Not Met) • Final Report (Not Met) • Presentation for Industrial Review Panel (Partially Met) • 80% of Presentation Completed
Possible Future Work Recommendations for Further Work • Explore alternative learning styles • More research tools in the field of artificial intelligence
Human Budget Human Budget
Financial Budget Financial Budget
Lessons Learned Lessons Learned • Establish two weekly meetings • Progress meeting with faculty advisors • Development meeting with team members • Wounded team members don’t improve group efficiency • Keep faculty advisors well-informed of progress and seek feedback • Plan to complete milestones ahead of schedule • Balance workload among team members
Closing Summary Closing Summary • New way of looking at neural networks • Overcomes limitations of traditional neural nets • Can be greatly reused and built upon • Will further the field of Artificial Intelligence
Questions? Questions?
