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Artificial Neural Networks CSC 562: Final Project Dave Pizzolo What is an Artificial Neural Network? 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 Typical ANN Applications 1. Function Approximation 2. Classification 3. Time Series Prediction 4. Data Mining Not this ANN 1 - Function Approximation • 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 2 - Classification • 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} 3 - Time Series Prediction • 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 This Mackey Not this Mackey 4 - Data Mining • 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 NeuroDimension, Inc. 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 Demo Function Approximation • NS Excel • File --> Open --> BodyFat.xls • NeuroSolutions --> Train Network --> Train • Apply Production Dataset Time Series Prediction • File --> Open --> 2 TDNN CHAOS.NSB • Highlight range • Step Epoch Data Mining • File --> Open --> 48 CLUSTERING.NSB • Reset Classification • Step Epoch • File --> Open --> OCR.NSB • Tools --> Customize --> control Sample Problem • Start • Tools --> Neural Expert • Reset + Zero Count • Function Approximation --> Next • Step Exem • 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