1/ Gold Prize Awarded A Self-Organized Network Inspired by Immune Algorithm 免疫アルゴリズムに基づく自己組織化ネット ワーク Muhammad Rahmat WIDYANTO (01M35636) Hirota Laboratory Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology

2/21Background SONIA [proposed] Experiments ConclusionsContents

3/21 BP-NN [Rumelhart, 86] Global Response Overfitting Low Generalization Immune Algorithm [Timmis, 01] Local Response Characteristics only Diverse Representation Background (1/2)

4/21BP-NN ImmuneAlgorithm SONIA A Self-Organized Network inspired by Immune Algorithm [proposed] Better Recognition Better Generalization Background (2/2)

5/21 ・・・・・・ ・・・・・・ ・・・・・・ Input layer Hidden layer Output layer BP-NN : [Rumelhart, 86] SONIA [proposed] (1/5) Input Vector Hidden Unit Antigen Immune Algorithm : [Timmis, 01] Recognition Ball (RB)

6/21 Input Vector Unit Centroid Hidden Unit Recognition Ball (RB) B Cell Antibody Antigen Paratope Epitope Euclidian Distance SONIA [proposed] (2/5)

7/21 Antibody Generation Antigen [1..m] SONIA [proposed] (3/5) Input Vector [1..m] Hidden Unit Creation Hidden Unit 1 Hidden Unit 2 Hidden Unit i Mutated Hidden Unit n RB 2 RB i Mutated RB n RB 1 B Cell Mutation B Cell Construction

8/21 Initialization, 1 st Unit : 1.Number of Vectors 2.Unit Centroid 1.Distance Calculation 2.Minimum Distance Next input From first input Hidden Unit Creation Distance ? Stimulation SONIA [proposed] (4/5) A New Unit 1.Number of Vectors 2.Unit Centroid  Unit Updating 1.Number of Vectors 2.Unit Centroid 

9/21 New Units: 1. Initial Point 2. Ending Point Distance > Level Number of Vectors < Threshold For every two adjacent units No A Mutated Unit 1.Number of Vectors 2.Unit Centroid Yes SONIA [proposed] (5/5) Mutated Hidden Unit Creation

10/21 Experiments Sinusoidal Problem [Holmstrom, 92] [Karystinos, 00] h(x) = 0.4 sin (2  x) + 0.5, x  [0,1] h(x)h(x) x Real function Training Data

11/21 Bayesian Regularization [MacKay, 92] Improving Generalization Automatic Determination of  Automatic Determination of  MSEreg =  MSE + (1-  ) MSW MSEreg =  MSE + (1-  ) MSWExperiments

12/21 BP-NN [Rumelhart, 86] Approximation Error : x h(x)h(x) Training Data Approximation BP-NN Regularization [MacKay, 92] Approximation Error : x h(x)h(x) SONIA without mutation Approximation Error : x h(x)h(x) SONIA with mutation [Proposed] Approximation Error : x h(x)h(x)

13/21 Improving GeneralizationExperiments Effect of Mutated Hidden Unit SONIA without Mutated Hidden Unit x h(x)h(x) SONIA with Mutated Hidden Unit [proposed] x h(x)h(x) Training Data Approximation

14/21 Experiments Ministry of Agriculture Project Supermarket Food Store Market Area Production Area Frozen TruckPerishable Food Quality Control Server Prediction Engine: Neural Networks QualityCheck

15/21 Akita Chiba Experiments Real Food Quality Control Data Data Lodger  Channel 1 : Meat surface  Channel 2 : Packaging box October – December 2001  15 Delivery Data

16/21 Data Collection : Data Lodger Channel 1 Channel 2 Time-temperature Data Time oCoC ( X 5 Minutes ) Feature Extraction : Mean & Standard Deviation Pre-Processing : Range Selection Range Selected Experiments Prediction System [proposed] A B C D E Neural Networks ch1:Mean ch1:SD ch2:Mean ch2:SD Quality good

17/21 TOP MIDDLE BOTTOM Experiments Recognition Percentage

18/21 SONIA is proposed 20% Recognition Improvement Conclusions Approximation Error is 1/24 times lower The World First Time-temperature based Food Quality Control Application

19/21 Research Plan (1/2) Forest Fires and Rainfall Prediction

20/21 Prof. Hirota Laboratory Tokyo Institute of Technology Prediction Engine Analysis Dr. Kusumputro Laboratory The University of Indonesia Data Collection Pre-Processing Research Plan (2/2) International Collaborative Research

21/21 International Conference Papers R. Widyanto, Megawati, Y. Takama, and K. Hirota, "A time-temperature-based food quality prediction using a self-organized network inspired by immune algorithm", In Proceedings of the 1st International Conference on Soft Computing and Intelligent Systems, Tsukuba, Japan, R. Widyanto, Megawati, K. Kawamoto, and K. Hirota, "Clustering analysis using a self-organized network inspired by immune algorithm", In Proceedings of the IASTED International Conference on Artificial and Computational Intelligence, Tokyo, Japan, ACTA Press, pp , Other Presentation & Research Report R. Widyanto, Megawati, Y. Takama and K. Hirota, "Quality prediction of food product based on time-temperature data using SONIA neural network", Final Research Report, Japan Ministry of Agriculture, Japan, Publications

22/21 SONIA [proposed] Hidden Unit w1w1 Input Vector s1s1 s2s2 x1x1 w2w2 Local Response (SONIA) x 1 = f( ((s 1 - w 1 ) 2 + ( s 2 - w 2 ) 2 ) 1/2 ) f : tangent sigmoid function Global Response (BP-NN) x 1 = g( s 1 w 1 + s 2 w 2 +  ) g : log sigmoid function

23/21 PC Pentium IV 2 Ghz 128 MB Memory Matlab 6.1 C/C++ Library on Windows 2000 SONIA : Stimulation = 0.05, Level = 0.05, Threshold = 2 SONIA without mutation : 10 hidden units SONIA without mutation : 27 hidden units BP and BP with regularization : 27 hidden units 1000 iterations for 10 trials Experiments Sinusoidal Problem

24/21 Method Learning Time (seconds) BP-NN8.42 BP-NN with Regularization19.72 SONIA without Mutation11.07 SONIA with Mutation27.83 Experiments Learning Time

25/21 Depends on random initialization 2 of 10 trials fail for correct approximation Experiments Bayesian Regularization [MacKay, 92]

26/21 PC Pentium III 600 Mhz 64 MB Memory Matlab 6.1 C/C++ Library on Windows 2000 SONIA : Stimulation = 0.03, 9 neurons SONIA with mutation cannot be applied BP-NN : 9 neurons 2600 iterations Experiments Real Food Quality Control Data

27/21 Region SONIA (seconds) BP-NN (seconds) TOP MIDDLE BOTTOM Average Experiments Learning Time