Neural Networks

Brian Merrick3-25-10

CS498 Seminar

Outline Introduction to Neural Networks Types of Neural Networks Neural Networks with Pattern Recognition

Applications of Neural Networks Conclusion Questions

Introduction to Neural Networks

Inspiration for development came from attempts to model the human central nervous system

Artificial network that simulates systems, such as how the brain processes information

Composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems

Introduction to Neural Networks

Each neural network consists of many input nodes whose input can to go one or more processing nodes to produce output

Introduction to Neural Networks

Uses learning algorithms to compute output values based on result of the previous populations

Not rule-based like a traditional system, but trained to recognize and generalize the relationship between a set of inputs and outputs

Types of Neural Networks Prediction Classification Data Filtering Supervised Learning Unsupervised Learning

Types of Neural Networks Prediction

• Use input values to predict some output (e.g. pick the best stocks in the market, predict weather, identify people with cancer risks etc.)

• Example Networks: Directed Random Search

Types of Neural Networks Classification

• Use input values to determine the classification (e.g. is the input the letter A, is the blob of video data a plane and what kind of plane is it)

• Example Networks: Learning Vector Quantization Probabilistic Neural Networks

Types of Neural Networks Data Filtering

• Smooth an input signal (e.g. take the noise out of a telephone signal)

• Example Networks: Recirculation

Supervised Learning Uses a known structure with random weights. The inputs and outputs are known

The data set is large enough to complete learning and can be tested later for accuracy of computed outputs

The network adjusts weight values to some predetermined level of accuracy and then stops

Unsupervised Learning Seeks to determine how the data is organized

An answer is requested from the neural network and weights are adjusted if the answer is not ‘correct’

Uses back-propagation for each iteration in the network

Neural Networks with Pattern Recognition

To begin, the network is initialized, all the connection strengths are set randomly, and the network sits as a blank slate

The network is then presented with information and the input nodes receive a digitized version of the image

Neural Networks with Pattern Recognition

In a gender pattern recognizer each response will be compared to the correct response for that picture (i.e., 0.0 for male, 1.0 for female) and each connection strength is adjusted so that next time it's shown that picture

Applications of Neural Networks

Character Recognition: handwriting recognition, number recognition

Image (Data) Compression: can compress and decompress image data

Pattern Recognition: rare coin evaluation, bomb sensing equipment in airports

Application of Neural Networks Signal Processing: removing telephone background noise, detecting engine misfires by sound in real-time

Finance: market forecasting, credit history checks, loan approval, telemarketing

Systems Control: factories, refineries, NASA space shuttle, robotics

Conclusion Neural networks try to simulate tasks of the human brain

Neural networks are very complex to implement because of the relationships between the data and the learning nodes

Unsupervised neural networks are the closest solution to modeling the human brain

Questions?

References Neural Networks. http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html#Introduction%20to%20neural%20networks

Artificial Neural Networks Technology. http://nature.berkeley.edu/~bingxu/UU/

geocomp/Week14/Network%20Selection%205_0.htm The Neural Approach to Pattern Recognition. http://www.acm.org/ubiquity/views/v5i7_jesan.html