A Self-Organized Network inspired by Immune Algorithm

1. ASelf-Organized Network inspired by Immune Algorithm 2002年度　修士論文発表　金賞受賞 M. Rahmat WIDYANTO　（Ｄ１） Hirota Laboratory Computational Intelligence & Systems Science Tokyo Institute of Technology

2. Contents • Theoretical Section • Application Section • A Time-Temperature-based Food Quality Prediction using a Self-Organized Network inspired by Immune Algorithm R. Widyanto, Megawati, Y. Takama, K. Hirota (to be submitted to International Conference on Soft Computing and Intelligent System 2002, Tsukuba, Japan) • A Self-Organized Network inspired by Immune Algorithm for Clustering Analysis R. Widyanto, Megawati, K. Hirota (to be submitted to The 2002 IEEE International Conference on Data Mining, Maebashi City, Japan) • Generalization Improvement of Prostate Cancer Prediction using a Self-Organized Network inspired by Immune Algorithm (Future Work)

3. Theoretical Section: Background Self-Organized Network [Kohonen, 1996] • Number of neurons should be decided • Describes characteristics from trained data only • Low Generalization Ability • Improved Version of Self-Organized Network • Neurons are automatically created • Generalization is improved Immune Algorithm [Timmis, 2001] • First and Second Immune Responses • Automatic Creation of B-cells • Mutation of B-cells

4. Data Acquisition Pre-Process Feature Selection Neural Network Application Section (1):Prediction System

5. Application Section (1):Data Acquisition • Akita is known as a pork production area. • Everyday frozen trucks deliver the meat from Akita to Chiba. • During delivery, temperature inside the trucks is recorded every 5 minutes using data lodger. • Data lodger consists of two channels. • Channel 1 to measure the meat packaging box. • Channel 2 to measure the meat`s surface.

6. Application Section (1):Neural Network Combined with back-propagation output layer

7. Application Section (1):Experiment: Setting • For each region TOP, MIDDLE, BOTTOM, experiment is conducted separately. • From October to December 2001 there were 15 times meat deliveries from Akita to Chiba resulting 15 input data. • Recognition Experiment • Learning Phase: all 15 data trained to network • Testing Phase : all 15 data tested • Compare recognition obtained by SONIA network and standard back-propagation network. • The codes are implemented on PC (600 Mhz Processor, 64 MB RAM) using Matlab 6.1 under Windows 2000 operating system.

8. Application Section (1):Experiment: Error Convergence • Back-propagation : slower convergence (red-line) • SONIA network : faster convergence (green-line) BOTTOM TOP MIDDLE

9. Region SONIA Back-propagation TOP 100% 73.3% MIDDLE 93.3% 73.3% BOTTOM 100% 80% Application Section (1):Experiment: Recognition Result SONIA network outperformed back-propagation in recognition experiment.

10. Region SONIA Back-propagation Node Construction Learning Learning TOP 0.6 s 60.09 s 34.72 s MIDDLE 0.94 s 38.01 s 43.77 s BOTTOM 0.33 s 54.32 s 44.21 s Average 51.43 s 40.9 s Application Section (1):Experiment: Computation Time SONIA network slightly needed more computation time in average than back-propagation network. (s = seconds)