Pattern Recognition By

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PATTERN RECOGNITION BY

ARTIFICIAL NEURAL NETWORKING (ANN)



Project Assigned

Detection of faulty flow meters through patternrecognition using ANN.

Detection of Oil leaks in pipeline through patternrecognition using ANN



Pattern Recognition

Pattern recognition is "the act of taking in raw dataand taking an action based on the category of the

pattern´

Used to classify data (patterns) based either onprior knowledge or on statistical information

extracted from the patterns.



Applications of Pattern Recognition

REACTOR MODELING THROUGH IN SITU ADAPTIVE LEARNING

PREDICTING PLANT STACK EMISSIONS TO MEET ENVIRONMENTAL

LIMITS

PREDICTING FOULING / COKING IN FIRED HEATERS

PREDICTING OPERATIONAL CREDITS

FORECASTING PRICE CHANGES OF A COMPOSITE BASKET

OF COMMODITIES

CORPORATE DEMOGRAPHIC TREND ANALYSIS



Wavelet transform

The input data is collected by SCADA ( supervisorycontrol and data acquisition)This data is fed to Wavelet Transform

A wavelet is a small wave which oscillates and decaysin the time domain

´ ¹ º ¸

©ª¨

!=! t

x x dt s

t

t x s s sCWT X

] X X ] ] 1

),(),(

Continuous wavelettransform of the signal

x(t) using the analysiswavelet ] (.)

S cale = 1/frequency



Different wavelets are matched with analysiswindow

Analysis windows of different lengths are used fordifferent frequencies:Analysis of high frequencies Use narrower windows forbetter time resolutionAnalysis of low frequencies Use wider windows forbetter frequency resolution

The function used to window the signal is called thewavelet



Few wavelets

Haar Wavelet

Daubechies-4

Mexican Hat

Daubechies-10 Daubechies-40



Applications of wavelet transforms

CompressionDe-noisingFeature ExtractionDiscontinuity DetectionDistribution EstimationData analysis

Biological dataNDE dataFinancial data



Wavelets at work !!



Principal Component Analysis(PCA)

Principal component analysis (PCA) -mathematicalprocedure that transforms a number of possiblycorrelated variables into a smaller number of

uncorrelated variables called principal componentsPCA is mathematically defined as an orthogonal lineartransformation that transforms the data to a newcoordinate system such that the greatest variance byany projection of the data comes to lie on the firstcoordinate (called the first principal component), thesecond greatest variance on the second coordinate, andso on



Computing PCA

Organize the data set- m x n Matrixwhere m ² No. of variables, N ² no of data pointsCalculate the empirical mean ² m x 1 matrix

Calculate the deviations from the meanFind the covariance matrixFind the eigenvectors and eigenvalues of the covariance

matrix ² m x m matrix

Rearrange the eigenvectors and eigenvalues-Sort the columns of the eigenvector matrix and eigenvalues

matrix in order of decreasing eigenvalues



Compute the cumulative energy content for eacheigenvectorSelect a subset of the eigenvectors as basis vectorsConvert the source data to z-scoresProject the z-scores of the data onto the new basis

Y = W*ZWhere, Y - PCA matrix

W*- conjugate transpose of Eigen vector matrixZ ² z score matrix



Introduction To ANN

Artificial neural network (ANN) is a mathematicalmodel or computational model simulating the structureand/or functional aspects of biological neural networks

ANN is an adaptive system -changes its structure basedon external or internal information during learning



Learning

Neural nets mimic human learning processes .Nets are trained iteratively on input data along withthe corresponding target outcomes.After a sufficient number of training iterations, netslearn to recognize patterns creating internal modelsof the processes governing the data.

Two different modes of adaptive learning- Supervised Learning- Unsupervised Learning



Supervised Learning

S upervised learning is a learning technique fordeducing a function from training data.The training data consist of pairs of input objects

(typically vectors), and desired outputsDuring the training process, the differences between theactual output from the net and the desired targetoutcomes) are propagated backwards through the netand are used to update the connecting weights.Repeated iterations of this operation result in a

converged set of weights and a net that has beentrained to identify and learn patterns





Unsupervised Learning

Unsupervised learning involves extraction ofcharacteristic features from a large number ofcases and the subsequent organization of thesecases into groups sharing similar attributesHence in this case , input data points and the

weight functions are provided .This type of learning

leads to cluster formationDifferent techniques used for unsupervised learning

are Radial Basis Function (RBF) and Artificialresonance technique (ART)



Clustering at a glance !



Clustering

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Pattern Recognition By

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