1. Neural Information Systems ANSER :Rainfall Estimating System THONN:Financial Date Simulation System FACEFLOW: Face Recognition system System Dr. Ming Zhang, Associate Professor Department of Physics, Computer Science & Engineering Christopher Newport University 1 University Place, Newport News, VA 23606, USA Dr. Ming Zhang

2. u 11/1999 – 07/2000: Senior USA NRC Research Associate NOAA, Funding $70,000. u 03/1995 – 11/1999: Ph.D. Supervisor and Senior Lecturer University of Western Sydney Fundings: A$203,724 Cash from Fujitsu, ARC, & UWSM u 07/1994-03/1995: Ph.D. Supervisor and Lecturer Monash University, A$50,000 Grant from Fujitsu) u 11/1992-07/1994: Project Manager & P.H.D. Supervisor University of Wollongong, (A$850,000 from SITA) u 07/1991-10/1992: USA NRC Postdoctoral Fellow NOAA, Funding: US$100,000) u 07/1989-06/1991: Associate Professor and Postdoctoral Fellow The Chinese Academy of the Sciences. Funding: RMB$2,000,000 Dr. Ming Zhang

3. Artificial Neural Network Techniques For Estimating Heravy Rainfall From Satellite Data Ming Zhang, Roderick A. Scofield NOAA/NESDIS/ORA 5200 Auth Road, Room 601 Camp Springs, MD 20746, USA Email: roderick.scofield@noaa.gov Page 1

4. Page 2 ANSER System Interface

5. Artificial Neural network expert System for Estimation of Rainfall from the satellite data ANSER System (1991-2000) - 1991-1992:US$66,000 suported by USA National Research Council & NOAA - 1995-1996:A$11,000 suppouted by Australia Research Council& NOAA - 1999-2000:US$62,000 suported by USA National Research Council & NOAA Page 3

6. Why Develop ANSER ? - More than $3.5 billion in property is damaged and, more than 225 people are killed by heavy rain and flooding each year - No rainfall estimating system in GIS system, No real time and working system of rainfall estimation in the world - Can ANN be used in the weather forecasting area? If yes, how should we use ANN techniques in this area? Page 4

7. Why Use Neural Network Techniques ? - Two Directions of New generation computer Quamtun Computer Artificial Neural Network - Much quicker speed ? - Complicated pattern recognition? - Unknown rule knowledge base? - Self learning reasoning network? - Super position for multip choice? Page 5

8. Page 26 ANSER Rainfall Estimation Result (May 2000) Time LAT LAN ANSER GAGE Min 0918 37.032 87.906 1.47mm 2.0mm Max 0918 38.765 88.480 6.37mm 6.0mm Min 1207 42.866 90.959 2.45mm 2.0mm Max 1207 42.837 87.403 9.31mm 12.0mm Min 2306 36.599 85.463 0.98mm 1.8mm Max 2306 38.621 86.936 8.82mm 9.0mm Min 2406 34.144 84.349 7.10mm 6.0mm Max 2406 37.148 88.696 27.69mm 33.0mm

9. Page 29 ANSER Rainfall Estimation Result 9th May 2000 Time: 18Z LAT LAN Min 37.032 87.906 Max 38.765 88.480 ANSER Min: 1.47 mm Max: 6.37mm NAVY Min: 2.0mm Max: 6.0mm

10. Page 30 ANSER Rainfall Estimation Result 12th May 2000 Time: 07Z LAT LAN Min 42.866 90.959 Max 42.837 87.403 ANSER Min: 2.45 mm Max: 9.31mm Gage Min: 2.0mm Max: 12.0mm

11. Page 31 ANSER Rainfall Estimation Result 23th May 2000 Time: 06Z LAT LAN Min 36.599 85.463 Max 38.621 86.936 ANSER Min: 0.98 mm Max: 8.82mm Gage Min: 1.8mm Max: 9.0mm

12. Page 32 ANSER Rainfall Estimation Result 24th May 2000 Time: 06Z LAT LAN Min 34.144 84.349 Max 37.148 88.696 ANSER Min: 7.10 mm Max: 27.69mm Gage Min: 6.0mm Max: 23.0mm NAVA Min: 7.0mm Max: 33.0mm

13. Conclusion- What Approved Artificial Neural Network Techniques can : - Much quick speed: 5-10 time quick - Complicated pattern recognition: cloud merger - Unknown rule knowledge base: Rainfall - Reasoning network: rainfall estimation Page 27

14. Conclusion- Next Step - Rebuild interface & retraining neural networks - New neural netowrk models: more complicated pattern recognition - Self expending knowledge base: attract knowledge from real time cases - Self learning reasoning network: automatic system to - Study in advance in 15 years: Artificial Neural Network - one of two directions of new generation computer Research Page 28

15. Using PT-Honn Models For Multi-polynomial Function Simulation Bo Lu, Hui Qi, Ming Zhang University of Western Sydney Macarthur Campbelltown, NSW 2560, Australia Roderick A. Scofield NOAA/NESDIS/ORA 5200 Auth Road,Camp Springs, MD 20746, USA

16. u PHONN Simulator (1994 - 1996) - Polynomial Higher Order Neural Network financial data simulator - A$ 105,000 Supported by Fujitsu, Japan u THONN Simulator (1996 - 1998) - Trigonometric polynomial Higher Order Neural Network financial data simulator - A$ 10,000 Supported by Australia Research Council u PT-HONN Simulator (1999 - 2000) - Polynomial and Trigonometric polynomial Higher Order Neural Network financial data simulator - US$ 46,000 Supported by USA National Research Council

17. PT-HONN Data Simulator

18. Simulating by PT-HONN Simulator

19. Structure of PT-HONN Page 17

20. Cloud Merger Operator u MI(i, j) is a black-and-white image which can be used to represent asatellite image. u Label Set:L = {0, 1, 2, …… M} Each label corresponds to a different cloud merger. u Cloud Merger Recognising operator CMR CMR: MI(i, j)  L Page 7

21. Cloud Merger Operator Set u The cloud merger recognising operator CMR is the operator set: u CMR = { CMCI, CMR1, CMR2, CMS1,CMS2,CMS3,CMS4, CMM1,CMM2,CMM3,CMM4} u Where CMCI: Circle input satellite data cloud merger recognising operator. …... Page 8

22. Ternary Output of Cloud Merger Operator  1, O(Ns,t)   1 – cloud merger u L=  2, O(Ns,t)   3 – further test needed  0, O(Ns,t)   2 - cloud not merger u where s-th level is the output layer of NN. u All other operators (CMR1, CMR2, CMS1, CMS2, CMS3, CMS4,CMM1, CMM2, CMM3, CMM4) have the same definitions as CMCI. Page 9

23. Cloud Merge Using ANN Circle Operator Page 10

24. Cloud Merge Using ANN S-Shape 4 Operator Page 11

25. Results of Cloud Merger Operator ANN Operator No Merger Merger u Circle 0.0133980.988092 u Rectangle 10.0177900.984671 u Rectangle 20.005251 0.977073 u S-Shape 10.0105660.973505 u S-Shape 20.0246520.932447 u S-Shape 30.0097570.934428 u S-Shape 40.0322930.752404 u Moon-Shape 10.0056020.967767 u Moon-Shape 20.0386060.909732 u Moon-Shape 30.3666310.953632 u Moon-Shape 40.0055300.932430 Page 12

26. PT-HONN MODEL u The network architecture of PT-HONN has combined both the characteristics of PHONN and THONN. u It is a multi-layer network that consists of an input layer with input-units, and output layer with output-units, and two hidden layers consisting of intermediate processing units. Page 15

27. Definition of PT-HONN Page 16

28. Knowledge of Rainfall Half Hour Rainfall Inches Cloud Top Cloud Growth Latitude Degree Temperature 2/3 1/3 0 u > -32  C0.05 0.05 0.03 u -36  C0.20 0.13 0.06 u -46  C0.48 0.24 0.11 u -55  C0.790.43 0.22 u -60  C0.94 0.65 0.36 u -70  C 1.55 0.85 0.49 u <-80  C 1.93 0.95 0.55 Page 18

29. PT-HONN Results Cloud Top Cloud Growth PHONN PT_HONN Temperature Latitude Degree |Erro|r% |Error| % …… …… ……. …... u > - 32  C1/3 10.47 10.11 u - 36  C1/33.50 4.25 u - 46  C1/33.52 4.63 u - 55  C1/30.22 2.04 u - 60  C1/33.21 0.30 u - 70  C1/39.01 5.08 u < - 80  C1/3 3.89 1.21 u …… …… …… …... u Average 6.36% 5.68% Page 19

30. CONCLUSION u The results of the comparative experiments show that THONG system is able to simulate higher frequency and higher order non-linear data, as well as being able to simulate discontinuous data. u The THONG model can not only be used for financial simulation, but also for financial prediction. Using THONG System for Higher Frequency Non-liner Data Simulation & Prediction

31. FACEFLOW A Robot Vision System For Moving Face Recognition Dr. Ming Zhang

32. FACEFLOW (1992 - 2000) A computer vision system for recognition of 3-dimensional moving faces using GAT model (neural netowrk Groug-based Adaptive tolerance Tree) u A$850,000 supported by SITA (Society Internationale de Telecommunications Aeronautiques) u A$40,500 supported by Australia Research Council u A$78,000 supported by Australia Department of Education. u US$160,000 supported by USA National Research Council.

33. Page 20 Neuron-Adaptive Neural Network Simulator

34. * The network architecture of NANN is a multilayer feed-forward network that consists of an input layer with input-units, an output layer with output-units, and one hidden layer consisting of intermediate processing units. * There is no activation function in the input layer and the output neurones are summing units (linear activation) * our activation function for the hidden layer processing units is a Neuron-Adaptive Activation Function (NAAF) NANN Page 21

35. The activation function for the hidden layer processing units is a Neuron-Adaptive Activation Function (NAAF) defined as where a1,b1,a2,b2,a3 and b3 are real variable which will be adjusted (as well as weights) during training. NAAF Page 22

36. Structure of NANN Page 23

37. NANN Group u Neuron-Adaptive Feedforward Neural network Group (NAFNG) is one kind of neural network group in which each element is a neuron-adaptive feedforward neural network (F i ). We have: NAFNG ={F 1, F 2, F 3,…... F i,…...F n } Page 24

38. Feature of NANN u Hornik (1991): If the activation function is continuous, bounded and nonconstant, then standard FNN can approximate any continuous function. u Leshno (1993): A standard FNN can approximate any continuous function if the network's activation function is not a polynomial. u A neuron-adaptive feedforward neural network group with adaptive neurones can approximate any kind of piecewise continuous function. Page 25

39. u Neuron Network Group Models -complex system u GAT Tree Model - real time and real world face recognition u Neuron-Adaptive Neural Network Models - best match real world data u Center Of Motion Model - motion center u Second Order Vision Model - motion direction u NAAT Tree Model - a possible more powerful model for face recognition FACEFLOW: A Robot Vision System

40. Hornik, K. (1991) “Whenever the activation function is continuous, bounded and nonconstant, then for an arbitrary compact subset X R n, standard multilayer feedforward networks can approximate any continuous function on X arbitrarily well with respect to uniform distance, provided that sufficiently many hidden units are available”

41. Leshno, M. (1993) “A standard multilayer feedforward network with a locally bounded activation function can approximate any continuous function to any degree of accuracy if and only if the network’s activation function is not a polynomial”

42. Zhang, Ming (1995) “ Consider a neural network piecewise function group, in which each member is a standard multilayer feedforward neural network, and which has locally boundded, piecewise continuous (rather than polynomial) activation function and threshold. Eash such group can approximate any king of piecewise continuous function, and to any degree of accuracy”

43. Knowledge of Rainfall Half Hour Rainfall Inches Cloud Top Cloud Growth Latitude Degree Temperature 2/3 1/3 0 u > -32  C0.05 0.05 0.03 u -36  C0.20 0.13 0.06 u -46  C0.48 0.24 0.11 u -55  C0.790.43 0.22 u -60  C0.94 0.65 0.36 u -70  C 1.55 0.85 0.49 u <-80  C 1.93 0.95 0.55

44. PT-HONN Results Cloud Top Cloud Growth PHONN PT_HONN Temperature Latitude Degree |Erro|r% |Error| % …… …… ……. …... u > - 32  C1/3 10.47 10.11 u - 36  C1/33.50 4.25 u - 46  C1/33.52 4.63 u - 55  C1/30.22 2.04 u - 60  C1/33.21 0.30 u - 70  C1/39.01 5.08 u < - 80  C1/3 3.89 1.21 u …… …… …… …... u Average 6.36% 5.68%

45. Conclusion- What Approved Artificial Neural Network Techniques can : - Much quick speed: 5-10 time quick - Complicated pattern recognition: cloud merger - Unknown rule knowledge base: Rainfall - Reasoning network: rainfall estimation

46. Conclusion- Next Step - Rebuild interface & retraining neural networks - New neural netowrk models: more complicated pattern recognition - Self expending knowledge base: attract knowledge from real time cases - Self learning reasoning network: automatic system to - Study in advance in 15 years: Artificial Neural Network - one of two directions of new generation computer Research