Artificial Neural Networks:

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Presentation transcript:

Artificial Neural Networks: Algorithms & Hardware for Implementation By: Nathan Hower CSC 3990 – Computing Research Topics

What are Neural Networks? -Biological vs. Artificial -Computational models Components of a Neural Network - Inputs - Outputs - Transition Function (weights)‏

What are Artificial Networks Good For Anyway? - classification: pattern recognition - function approximation - clustering: data mining - association: restoring noisy data

Types of Artificial Neural Networks - Feedforward - Recurrent

Learning Paradigms and Algorithms -Supervised learning: sample results and actual results are compared - Backpropagation: error is corrected backwards -Unsupervised learning: unlabeled samples given, data organization is desired -Reinforcement learning: maximize 'reward' value

Parallel Computing for Processing Artificial Neural Networks - Multiprocessor computers: fast data communication - Heterogeneous clusters: indefinitely expandable - Beowulf clusters: best cost/performance ratio

Problematic Areas - Scalability Algorithms are designed for particular scope and/or limited hardware. - Computational power/cost Some problems are so complex that they require expensive specially designed hardware. - Lack of standardization The use of artificial neural networks is recent; alternate naming conventions and multiple equally viable approaches occur.

Current Areas of Application - Neurology & Neurobiology - Economics: stock market prediction - Image compression - NP-complete problems - EBAI – Studying Eclipsing Binaries with Artificial Intelligence

Future Work - Brain-Computer Interface (BCI)‏: neural network learning algorithms moves burden of learning to the computer - Mind uploading/brain simulation