TAILIEUCHUNG - SELF ORGANIZING MAPS APPLICATIONS AND NOVEL ALGORITHM DESIGNE Part 7

Tham khảo tài liệu 'self organizing maps applications and novel algorithm designe part 7', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 230 Self Organizing Maps - Applications and Novel Algorithm Design comprehensible because ship No. 5 is the oldest version of this vessel. Next the new group was created which was separated from the area activated before by ship No. 5. The new map of partition for area of activation looks like is presented on figure 15. S hip no. Data 1 2 3 4 5 presented before not presented before Table 3. The number of correct classifications Fig. 15. The new map of partition for area of activation for researched ships after introducing new ship 6. Conclusion As it is shown on results the used Self-Organizing Map is useful for ships classification based on its hydroacoustics signature. Classification of signals that were used during learning process characterize the high number of correct answer above 90 what was expected. This result means that used Kohonen network associated with feedforward network has been correctly configured and learned. Presentation of signals that weren t used during learning process gives lowest value of percent of correct answer than in previous case but this results is very high too about 70 of correct classification . This means that neural classifier has good ability to generalize the knowledge. More over after Ship s Hydroacoustics Signatures Classification Using Neural Networks 231 presentation of new ship which weren t taking into account during creating classifier the Kohonen networks was able to create new group dividing the group which belongs to the similar type of ship. After few cycles used neural networks expand its output vector or in other words map of membership about new area of activation. This means that used Kohonen networks has possibility to develop its own knowledge so it cause that presented method of classification is very flexible and is able to adaptation to changing conditions. Presented case is quite simple because it not take into account that object sounds change with time

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