1. CSC 562: Final Project Dave Pizzolo Artificial Neural Networks

2. 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?

3. 1.Function Approximation 2.Classification 3.Time Series Prediction 4.Data Mining Typical ANN Applications Not this ANN

4. 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

5. 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

6. 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

7. 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

8. 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)http://en.wikipedia.org/wiki/NeuroDimension Company website: http://www.nd.com/ http://www.nd.com/ Product NeuroSolutions: http://www.neurosolutions.com/ http://www.neurosolutions.com/ 30 minute video demo: http://www.neurosolutions.com/resources/videotour.html# http://www.neurosolutions.com/resources/videotour.html# FREE evaluation copy of software: http://www.nd.com/neurosolutions/download.html http://www.nd.com/neurosolutions/download.html Sample data: http://www.nddownloads1.com/videos/NNAndNSIntroductionFiles.zip http://www.nddownloads1.com/videos/NNAndNSIntroductionFiles.zip NeuroDimension, Inc.

9. Demo Function 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