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

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)

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

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

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.

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

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.

Application Section (1): Experiment: Error Convergence Back-propagation : slower convergence (red-line) SONIA network : faster convergence (green-line) TOPMIDDLE BOTTOM

Application Section (1): Experiment: Recognition Result SONIA network outperformed back-propagation in recognition experiment. RegionSONIABack-propagation TOP100%73.3% MIDDLE93.3%73.3% BOTTOM100%80%

Application Section (1): Experiment: Computation Time SONIA network slightly needed more computation time in average than back-propagation network. (s = seconds) RegionSONIABack- propagation Node ConstructionLearning TOP0.6 s60.09 s34.72 s MIDDLE0.94 s38.01 s43.77 s BOTTOM0.33 s54.32 s44.21 s Average51.43 s40.9 s