Date Simulation System - Distributed Operating Systems - Lecture Slides, Slides of Computer Science

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Neural Information Systems

ANSER :Rainfall Estimating System THONN:Financial Date Simulation System

FACEFLOW: Face Recognition system System

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ANSER System Interface

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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?

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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?

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ANSER Rainfall Estimation Result

9th May 2000 Time: 18Z LAT LAN Min 37.032 87. Max 38.765 88. ANSER Min: 1.47 mm Max: 6.37mm NAVY Min: 2.0mm Max: 6.0mm Docsity.com

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ANSER Rainfall Estimation Result

12th May 2000 Time: 07Z LAT LAN Min 42.866 90. Max 42.837 87. ANSER Min: 2.45 mm Max: 9.31mm Gage Min: 2.0mm Max: 12.0mm Docsity.com

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ANSER Rainfall Estimation Result

24th May 2000 Time: 06Z LAT LAN Min 34.144 84. Max 37.148 88. ANSER Min: 7.10 mm Max: 27.69mm Gage Min: 6.0mm Max: 23.0mm NAVA Min: 7.0mm Max: 33.0mm Docsity.com

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

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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

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• PHONN Simulator (1994 - 1996)
• Polynomial Higher Order Neural Network financial data simulator
• A$ 105,000 Supported by Fujitsu, Japan
• THONN Simulator (1996 - 1998)
• Trigonometric polynomial Higher Order Neural Network financial data simulator
• A$ 10,000 Supported by Australia Research Council
• 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

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Simulating by PT-HONN Simulator

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Structure of PT-HONN

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Cloud Merger Operator Set

• The cloud merger recognising operator CMR is the operator set:
• CMR = { CMCI, CMR1, CMR2,

CMS1,CMS2,CMS3,CMS4, CMM1,CMM2,CMM3,CMM4 }

• Where

CMCI: Circle input satellite data cloud merger recognising operator. …...

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Ternary Output of Cloud Merger Operator

 1, O(Ns,t) ∈ ξ 1 – cloud merger

• L =  2, O(Ns,t) ∈ ξ 3 – further test needed  0 , O(Ns,t) ∈ ξ 2 - cloud not merger
• where s-th level is the output layer of NN.
• All other operators ( CMR1, CMR2, CMS1 , CMS2, CMS3, CMS4,CMM1, CMM2, CMM3, CMM4) have the same definitions as CMCI.

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