1. 1 Pattern Recognition: Statistical and Neural Lonnie C. Ludeman Lecture 25 Nov 4, 2005 Nanjing University of Science & Technology

2. 2 Lecture 25 Topics 1.Describe LCLNET: A Neural Network Simulator 2.Demonstrate the simulator 3.Show a Simple design Prob. 5.18 4. Solve an Abstraction of an image classification problem. Prob 5.19 5. Design using a functional link net Prob 5.20 6. Design a classifier for vowels Prob. 5.37.

3. 3 Lecture 25Topics 1.Describe LCLNET: A Neural Network Simulator 2.Demonstrate the simulator 3.Show a Simple design Prob. 5.18 4. Solve an Abstraction of an image classification problem. Prob 5.19 5. Design using a functional link net 6. Design a classifier for vowel classification

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5. 5 Screen 1. For data selection, training or test selection, viewing data, creating small compatible data sets and file management Screen 2. For graphic display of Neural net structure during training including error “waterfall” and throttle for three speeds on graphics, direct access to changing of all parameters, viewing and storing weights of design, and easy run again button. (B) LCLNET has four active screens

6. 6 Screen 3. Shows outputs of each node for each input including per sample error for the training set. Screen 4. Shows the results of using a test set other than the training set on your design sample by sample

7. 7 5.18 Use LCL neural Simulator and Data Set XORN.DAT (a) 2 Layers (L=2) 3 nodes Layer 1, 1node output, Bipolar Activation, train by sample, n=0.7, m=0.8, tol=0.01, Ng-widrow on, maxit=8000 How many iterations until a design was obtained? (b) Illustrate your design. (c) Use n=0.7 and m=0.8 and structure above run the simulator 10 times and compute the average number of iterations for a design

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9. 9 (a) Use the LCL Neural Network Simulator to design a classifier for the three classes given (b) Use a logical expression to represent the solution using the binary training patterns. (c) Compare the structures of designs in (a) and (b). (d) If the training samples were not binary how could you solve the problem using a neural network? A logical design? (e) Classify the following patterns using the neural net design in (a). 5.19 Continued

10. 10 5.20 Use LCLNET to design a functional Link Neural Network for the XORN.DAT file. Use link as [y 1 y 2 y 3 y 4 ] T = [ x 1 x 2 x 1 x 2 1 ] T (a) Train the flat net with n=0.7, m=0.3, tol=0.001, ng-widrow-ON, maxit=5000 (b) Run 10 trial designs and compute the average number of iterations for a design. (c) Compare results with those of Problem 5.8.

11. 11 5.37 Design a classifier to separate speech signals into the vowels “a”, “e”, and “i”. Sample waveforms below Speech “snipets”

12. 12 5.37 Continued

13. 13 (a) Design a neural net to serve as a classifier using the training set Vowel2.dat. Show your design and the results in testing your design with the training set. (b) Using your design from part (a), classify the snipets given in vowel3.dat. (c) Determine the confusion matrix for your results. Did the results surprise you? Explain. 5.37 Continued

14. 14 (d) fvowelt2.dat and fvowelt3.dat are the magnitudes of some of the components of the Fourier Transform of vowelt2.dat and vowelt3.dat respectively. (e) Repeat your design using the Fourier Transform data and compare your results with the those determined in parts (a) and (b). (f) Comment on the importance of using different features for many pattern classification designs. 5.37 Continued

15. 15 Summary Lecture 25 1.Describe LCLNET: A Neural Network Simulator 2.Demonstrate the simulator 3.Show a Simple design Prob. 5.18 4. Solve an Abstraction of an image classification problem. Prob 5.19 5. Design using a functional link net Prob 5.20 6. Design a classifier for vowels Prob. 5.37.

16. 16 End of Lecture 25