1. NEURAL - FUZZY LOGIC FOR AUTOMATIC OBJECT RECOGNITION.

2. INTRODUCTION Artificial Neural Networks is a system modeled on the human brain. It is an attempt to simulate with specialized hardware or sophisticated software the multiple layers of simple processing elements called neurons. Fuzzy set theory resembles human reasoning in its use of approximate or corrupted data to generate decision. Integration of ANN and Fuzzy logic can provide a very efficient solution for Target recognition.

3. MODULES OF THE ATR SYSTEM Acquisition: Capturing data with a sensor. Transformation: Preprocessing of the image. Segmentation: Identifying regions of interest, using Freeman code for border detection. Geometric features: Usage of Hough Transform for identifying hidden lines also. Target Database: Tabulated values for each model. Model Matching: Matching measured values with tabulated values.

4. IMPLEMENTATION OF NEURAL - FUZZY LOGIC With the inclusion of fuzzy logic in the ATR system, even when one dimension is obscured a match can be made with the remaining two dimensions. Network is trained using a Back Propagation algorithm

5. BACK PROPAGATION ALGORITHM

6. OTHER APPLICATIONS Character Recognition Automatic Phonetic Recognition Facial Recognition Signature Recognition Fingerprint Recognition

7. CONCLUSION The computing world has a lot to gain from Neural Networks. Their ability to learn makes them flexible and very powerful. The most exciting aspect of Neural Networks is the possibility that some day ‘conscious’ networks may be produced. Neural Networks have a huge potential and we will get the best of them when integrated with computing, AI, Fuzzy logic and related subjects.

8. BIBLIOGRAPHY

9. PRESENTED BY  NAGINI INDUGULA (4/4 CSE) 98311A0515 Sree Nidhi Institute of Science and Technology Hyderabad

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