CSC 562: Final ProjectDave Pizzolo

Artificial Neural Networks

Definition•An Artificial Neural Network (ANN) is a computer program that can recognize patterns in a given collection of data and produce a model for that data.

It resembles the brain in two respects:1.Knowledge is acquired by the network through a learning process (trail and error)2.Interneuron connection strengths known as synaptic weights are used to store the knowledge

What is an Artificial Neural Network?

1. Function Approximation2. Classification3. Time Series Prediction4. Data Mining

Typical ANN Applications

Not this ANN

• You know your inputs and outputs, but do not know your function

• y = f(x) where• x is a set of numeric inputs• y is a set of numeric outputs• f() is an unknown functional relationship between the input and

the output• The ANN must approximate f() in order to find the appropriate

output for each set of inputs• Demo: Body Fat Percentage

1 - Function Approximation

• Similar to the function approximation except that the output is a “class”, thus they are discrete

• For example:• Outputs = on or off• Outputs = sick or healthy

• Demo: Optical Character Recognition (OCR)• 0 = {1,0,0,0,0,0,0,0,0,0}• 1 = {0,1,0,0,0,0,0,0,0,0}• …• 9 = {0,0,0,0,0,0,0,0,0,1}

2 - Classification

• Time Series Prediction is similar to function approximation except that time plays an important role

• In function approximation, information that is needed to create output is contained in the input

• Image processing• In time series prediction, information from the past is need to

determine the output• Stock price prediction

• Demo: Predict Mackey Glass Chaotic Signal• Chaos is a signal that has characteristics similar to randomness,

but can be predicted accurate in the short term (e.g. weather)• Accurate predictions can be made only a few samples in advance

3 - Time Series Prediction

Not this MackeyThis Mackey

• All three previous problems required a known output for each input

• In data mining, you do not know the answer ahead of time. You want to extract data from the input

• Clustering• Compression• Principal Component Analysis

• This type of a network is called “unsupervised” because there is no “teaching” signal

• Demo: Clustering with Competition• Clustering 2D data into N different regions• Use competitive (unsupervised) learning

4 - Data Mining

NeuroDimension, Inc.•A software development company headquartered in Gainesville, Florida and founded in 1991. It specializes in neural networks, adaptive systems, and genetic optimization and makes software tools for developing and implementing these artificial intelligence technologies. (http://en.wikipedia.org/wiki/NeuroDimension)•Company website: http://www.nd.com/

Product•NeuroSolutions: http://www.neurosolutions.com/•30 minute video demo: http://www.neurosolutions.com/resources/videotour.html#•FREE evaluation copy of software: http://www.nd.com/neurosolutions/download.html•Sample data: http://www.nddownloads1.com/videos/NNAndNSIntroductionFiles.zip

NeuroDimension, Inc.

DemoFunction Approximation• NS Excel• File --> Open --> BodyFat.xls• NeuroSolutions --> Train Network -->

Train• Apply Production Dataset

Classification• File --> Open --> OCR.NSB• Tools --> Customize --> control• Start• Reset + Zero Count• Step Exem

Time Series Prediction• File --> Open --> 2 TDNN CHAOS.NSB• Highlight range• Step Epoch

Data Mining• File --> Open --> 48 CLUSTERING.NSB• Reset• Step Epoch

Sample Problem• Tools --> Neural Expert• Function Approximation --> Next• Browse --> MPGEvaluation.asc --> Next• Select All (but MPG) --> Next• Country --> Next• Use Input File for Desired File --> Shuffle Data Files --

> Next• MPG --> Next• Low --> Finish• Start• Testing --> Next --> Next --> Next --> Finish