Probalistic Neural Network Application to Warship Radio Station Identification
Naval University
Śmidowicza 69, Gdynia, Poland
e-mail: T.Praczyk@amw.gdynia.pl
Received:
Rec. September 1, 2005
DOI: 10.12921/cmst.2007.13.01.53-57
OAI: oai:lib.psnc.pl:631
Abstract:
The article presents use of PNN to identify ship’s radio stations. Two methods of PNN acceleration are included. The first one is a combination of PNN and Kohonen neural network. The task of Kohonen neural network is to roughly classify radio station. The final identification is performed by PNN. The second speeding up approach consists in using average data instead of original one. Moreover, a modification of PNN decision rule is applied.
Key words:
References:
[1] M. Butz, Rule-based Evolutionary Online Learning Systems: Learning Bounds, Classification and Prediction, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[2] S. Chalup, F. Maire, A Study On Hill Climbing Algorithms For Neural Network Training, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[3] P. Cichosz, Learning Systems, WNT Warsaw (2000) (In Polish).
[4] D. Curran, C. O’Riordan, Applying Evolutionary Computation to Designing Neural Networks: A Study of the State of the Art, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[5] K. Doherty, R. Adams, N. Davey, Hierarchical Growing Neural Gas, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[6] Y. Freund, H. Seung, E. Shamir, N. Tihsby, Selective sampling using the query by committee algorithm, Machine Learning, 28, pp. 133-168, (1997).
[7] B. Fritzke, A growing neural gas network learns topologies, Advances in Neural Information Processing Systems, MIT Press, Cambridge, (1995).
[8] L. Gajek, M. Kałuszka, Statistical Reasoning, WNT, Warsaw (2000) (In Polish).
[9] F. Gruau, Neural Networks Synthesis using Cellular Encoding and the Genetic Algorithm, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[10] B. Guinand, A. Topochy, K. Page, M. Burnham-Curtis, W. Punch, K. Scribner, Comparisons of Likelihood and Machine Learning Methods of Individual Classification, Scientific Literature Digital Library – http://citeseer.ist.psu.edu
[11] J. Korbicz, A. Obuchowicz, D. Uciński, Artificial neural networks, AOW PLJ, Warsaw (1994) (in Polish).
[12] J. Kornacki, J. Ćwik, Statistical learning systems, WNT, Warsaw (2005) (In Polish).
[13] G. Kuchariew, Processing and analysis of digital images, Szczecin Univ. of Technology, Szczecin (1999) (in Polish).
[14] M. Mandischer, Representation and Evolution of Neural Networks, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[15] D. Michie, D. Spiegelhalter, C. Taylor, Machine Learning, Neural And Statistical Classification, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[16] S. Osowski, Neural networks in algorithmic sense, WNT, Warsaw (1996) (In Polish).
[17] R. Parekh, J. Yang, V. Honavar, Constructive Neural Network Learning Algorithms for Pattern Classification, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[18] R. Polikar, L. Udpa, S. Udpa, Learn++: An Incremental Learning Algorithm for Supervised Neural Networks, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[19] M. Raymer, L. Kuhn, W. Punch, Knowledge Discovery in Biological Datasets Using a Hybrid Bayes Classifier/-Evolutionary Algorithm, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[20] R. Tadeusiewicz, M. Flesiński, Image recognition, PWN, Warsaw (1991) (in Polish).
[21] D. Wilson, T. Martinez, Reduction Techniques for Instance-Based Learning Algorithms, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
The article presents use of PNN to identify ship’s radio stations. Two methods of PNN acceleration are included. The first one is a combination of PNN and Kohonen neural network. The task of Kohonen neural network is to roughly classify radio station. The final identification is performed by PNN. The second speeding up approach consists in using average data instead of original one. Moreover, a modification of PNN decision rule is applied.
Key words:
References:
[1] M. Butz, Rule-based Evolutionary Online Learning Systems: Learning Bounds, Classification and Prediction, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[2] S. Chalup, F. Maire, A Study On Hill Climbing Algorithms For Neural Network Training, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[3] P. Cichosz, Learning Systems, WNT Warsaw (2000) (In Polish).
[4] D. Curran, C. O’Riordan, Applying Evolutionary Computation to Designing Neural Networks: A Study of the State of the Art, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[5] K. Doherty, R. Adams, N. Davey, Hierarchical Growing Neural Gas, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[6] Y. Freund, H. Seung, E. Shamir, N. Tihsby, Selective sampling using the query by committee algorithm, Machine Learning, 28, pp. 133-168, (1997).
[7] B. Fritzke, A growing neural gas network learns topologies, Advances in Neural Information Processing Systems, MIT Press, Cambridge, (1995).
[8] L. Gajek, M. Kałuszka, Statistical Reasoning, WNT, Warsaw (2000) (In Polish).
[9] F. Gruau, Neural Networks Synthesis using Cellular Encoding and the Genetic Algorithm, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[10] B. Guinand, A. Topochy, K. Page, M. Burnham-Curtis, W. Punch, K. Scribner, Comparisons of Likelihood and Machine Learning Methods of Individual Classification, Scientific Literature Digital Library – http://citeseer.ist.psu.edu
[11] J. Korbicz, A. Obuchowicz, D. Uciński, Artificial neural networks, AOW PLJ, Warsaw (1994) (in Polish).
[12] J. Kornacki, J. Ćwik, Statistical learning systems, WNT, Warsaw (2005) (In Polish).
[13] G. Kuchariew, Processing and analysis of digital images, Szczecin Univ. of Technology, Szczecin (1999) (in Polish).
[14] M. Mandischer, Representation and Evolution of Neural Networks, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[15] D. Michie, D. Spiegelhalter, C. Taylor, Machine Learning, Neural And Statistical Classification, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[16] S. Osowski, Neural networks in algorithmic sense, WNT, Warsaw (1996) (In Polish).
[17] R. Parekh, J. Yang, V. Honavar, Constructive Neural Network Learning Algorithms for Pattern Classification, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[18] R. Polikar, L. Udpa, S. Udpa, Learn++: An Incremental Learning Algorithm for Supervised Neural Networks, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[19] M. Raymer, L. Kuhn, W. Punch, Knowledge Discovery in Biological Datasets Using a Hybrid Bayes Classifier/-Evolutionary Algorithm, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
[20] R. Tadeusiewicz, M. Flesiński, Image recognition, PWN, Warsaw (1991) (in Polish).
[21] D. Wilson, T. Martinez, Reduction Techniques for Instance-Based Learning Algorithms, Scientific Literature Digital Library, http://citeseer.ist.psu.edu
