Neural Networks And Its Applications By Dr. Surya Chitra

OUTLINE Introduction & Software Basic Neural Network & Processing –Software Exercise Problem/Project Complementary Technologies –Genetic Algorithms –Fuzzy Logic Examples of Applications –Manufacturing –R&D –Sales & Marketing –Financial

Neural Network Applications TECHNIQUE NAMEAPPLICATION Signal ProcessingSensor Data Analysis, Acoustic Failure Diagnosis, Fault Detection, Speech Recognition, Noise Cancellation, Radar Processing. Image Processing Machine Vision, Fingerprint Identification, Medical Image Processing, Industrial Inspection

Neural Network Applications (contd..) TECHNIQUE NAMEAPPLICATION Non-Linear ModelingRisk Analysis, Medical Diagnosis, Chemical Modeling, Real Estate Appraisal, Capacity Planning, Insurance Claim Validation. Time-series analysisEconomic Forecasting, Stock & Commodity Trading, Weather Prediction, Sales Forecasting.

Neural Network Applications (contd..) TECHNIQUE NAMEAPPLICATION Process/ Manufacturing Management Manufacturing Process Control, Process/Product Optimization, Robotic Process Control, Inventory Management, Product Quality Control ClassificationLoan Evaluation, Optical Character Recognition, Biological Cell Identification, Chemical Spectral Analysis, Customer Target Marketing, Fraud Detection.

NN Applications in Manufacturing Catalyst Manufacturing Improvement OBJECTIVE Minimize Precipitation Without Sacrificing Quality 4 Inputs – 3 Ingredients – Solvent 2 Outputs –Polymer Gel Time –Amount of Precipitation

NN Applications in Manufacturing Catalyst Manufacturing Improvement NN TRAINING Gel Time and Amount of Precipitation Data for Several Production Lots was used. RESULTS Using NN Model, Operating Conditions for Minimum Precipitation was Obtained. These Conditions were Verified with Experiments Before the Production Changes

NN Applications in R&D Development of Hydrogenation Kinetics OBJECTIVE Develop Kinetic Model to Increase Throughput for Hydrogenation Process 3 Inputs –Pressure – Temperature –Catalyst Load One Output –Rate Constant

NN Applications in R&D Development of Hydrogenation Kinetics Experimental Plan

NN Applications in R&D Development of Hydrogenation Kinetics Comparison of NN and Statistical Models

NN Applications in R&D Development of Hydrogenation Kinetics RESULTS Using NN Model, Operating Conditions for Hydrogenation Process to give Higher Reaction Rate and Thereby Higher throughput. These Conditions were Verified with Experiments Before the Production Changes.

NN Applications in Marketing Modeling Customer Behavior –Prospect Scoring –Retention & Loyalty Studies –Profitability Analysis –Credit Scoring –Delinquency & Behavior Scoring Database Enhancement –Patchy Database interpretation

NN Applications in Marketing (contd..) Customer Segmentation –Classify Customer-base (Unsupervised) –Database Enhancement Retail Modeling –Geo-demographic Classification –Small-area Modeling

NN Applications in Marketing (Contd..) Sales Analysis –Multivariable Sales Data Advertising & promotions Competition & macro-economics –Alternate to Time Series Forecasting Data Visualization –Distill Highly Noisy Data –Graphically Present Clean Data

NN Applications in Marketing Prospect Scoring Example Prospect Scoring –5 Million Customer Base –Initial Sample = 50,000 –1000 Responded (2%) –Need to Increase Response to 4% Information Gathered –Time Acct. Open (TIMEAC) –Avg. Acct. Balance (AVEBAL)

Prospect Scoring Example Average Account Balance Time Account Open, Years Respondents

Prospect Scoring Example Non-Respondents Average Account Balance Time Account Open, Years

Neural Networks in Marketing Prospect Scoring Example 2 Inputs (TIMEAC & AVEBAL) One Output (Score) –Zero for respondent –One for non-respondent Training Set (1000 Cases) –500 Randomly from Resp. Pool –500 Randomly from Non-resp. Pool

Prospect Scoring Example NN Model Fit

Neural Networks in Marketing Prospect Scoring Example Test Set (1000 Cases) –Reaming 500 respondents –500 Randomly from Non-respondents Predict as Gains Chart –Model Calculates SCORE for Test Set –Rank in Descending Order of SCORE –Add-up No. of Resp. & Non-resp. –Plot on the Chart

Prospect Scoring Example NN Model Gains Chart Cumulative Non-response (%) Cumulative Response, (%)

Neural Networks in Marketing Prospect Scoring Example Results –Mail Top 1 Million Savers –Generate 40,000 New Prospects –Cost Saving Pounds Conclusion –Alternate to Statistical Tools –Handle Large Amounts of Data

NN Applications in Finance Issues to Consider –Optimal Time Horizon SPIX into 10 days Forward –Input Set to Use Change in the Indicator Periodicity of Indicator How Much Lag NN for Managing Investments

NN for Managing Investments Example Inputs - 21 indicators –CRB Index, $ Index, Etc. (7) –Price, Volume, Put-Call Ratio (21) Time Lags –5, 10, 15, 20, & 25 Total of 126 Variables Predict 5 days forward Prediction of S&P 500 Index Futures

SUMMARY ANNs Universal Function Approximators –Even for non-linear functions –Can handle discontinuities Estimate Piece-wise Approximations Trigger & Use Specialized Models ANNs can be automated ANNs learn incrementally

SUMMARY (contd..) Changing Technology –ANN methodology changing Interpretation –Hard to interpret/ Physical meaning Number of Parameters –ANNs usially have more /Overfitting ANNs need more computer power