Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 67 - 72, 26.12.2016
https://doi.org/10.18201/ijisae.267039

Öz

Kaynakça

  • [1] Iyoda, E. M., Nobuhara, H. and Hirota, K.: A Solution for the N-bit Parity Problem Using a Single Translated Multiplicative Neuron, Neural Processing Letters, vol.18, pp. 213-218, 2003.
  • [2] Arslanov, M.Z., Ashigaliev, D.U. and Ismail, E. E.: N-bit Parity Ordered Neural Network, Neurocomputing 48 (2002), 1053-1056
  • [3] Al-Rawi, M.: A Neural Network to Solve the Hybrid N-parity: Learning with Generalization Issues, Neurocomputing, vol.68, pp. 273-280, 2005
  • [4] Hohil, M. E., Liu, D., Smith, S. H., Solving the N-bit parity problem using neural networks, Neural Networks, vol.12, pp.1321-1323, 1999.
  • [5] Li, D., Hirasawa, K., Hu, J., Murata, J., Studying the effects on multiplication neurons for parity problem, 41st Society of Instrument and Control Engineers-SICE,2002.
  • [6] Kim, K., Kim, S., Joo, Y., Oh, A.S.: Enhanced fuzzy single layer perceptron, Advances in Neural Networks, vol.3496,pp. 603-608, 2005.
  • [7] Setino, R.: On the solution of the parity problem by a single hidden layer feedforward neural network, Neurocomputing, vol.16 (3), pp. 225-235, 1997.
  • [8] Setiono, R., Hui, L. C. K.: Some N-bit parity problems are solvable by feed-forward networks with less than n hidden units, Int. Joint Conf. on Neural Networks, 1993, pp. 305-308.
  • [9] Schmitt, M.: On the complexity of computing and learning with multiplicative neurons, Neural Computation, vol.14(2), pp. 241-301, 2002.
  • [10] Zhang, B.-T.: A Bayesian Evolutionary Approach to The Design and Learning of Heterogeneous Neural Trees, Integrated Computer-Aided Engineering, vol. 9(1), pp. 73-86, 2002.
  • [11] Bas, E., Uslu, V. R. and Egrioglu, E.: Robust learning algorithm for multiplicative neuron model artificial neural networks, Expert Systems with Applications, vol.56, pp. 80-88, 2016.
  • [12] Thrun, S.B., Bala, J., Bloedorn, E., Bratko, I., Cestnik, B., Cheng, J., De Jong, K., Dzeroski, S., Fahlman, S.E., Fisher, D., Hamann, R., Kaufman, K., Keller, S., Kononenko, I., Kreuziger, J., Michalski, R.S., Mitchell, T., Pachowicz, P., Reich, Y., Vafaie, H., Van de Welde, W., Wenzel, W., Wnek, J., and Zhang, J.: The Monk’s Problems: A Perfor. Comparison of Different Learning Algorithm, a Report, Carnegie Mellon University CMU-CS-91-197, 1991.
  • [13] University of California, Irvine Dataset: [Online]. Available: ftp://ftp.ics.uci.edu/pub/machine-learning-databases/monks-problems/, retrieved November 16 2016.
  • [14] Pilgrim, R., A., “Tic-Tac-Toe: Introduction Expert Systems to Middle School Students”, Acm Sigcse Bulletin, Vol. 27, 340–344, (1995).
  • [15] Gordon, A., “A General Algorithm for Tic-Tac-Toe Board Evaluation”, Journal of Computing Sciences in Colleges, Vol. 21, 42-46, (2006).
  • [16] Solorio, T. and Fuentes, O.: Taking Advantage of Unlabelled Data with the Ordered Classification Algorithm, ACTA, Proc. of AI and Soft Computing ASC 2002, 357-200, (2002).
  • [17] Noughts And Crosses - The oldest graphical computer game, http://www.pong-story.com/1952.htm, retrieved November 16, 2016.
  • [18] Wachsmuth, B. G., Tic-tac-toe game, http://pirate.shu.edu/~wachsmut/Teaching/CSAS1111/Assigns-CPP/assign7.html, retrieved November 17, 2016.
  • [19] Massey, B., Tic-tac-toe board evaluation, http://web.cecs.pdx.edu/~bart/cs541-fall2001/homework/3-learn.html, retrieved November 17, 2016.
  • [20] Appel, A. W., Game player programs, http://www.cs.princeton.edu/courses/archive/spr05/cos217/asgts/gameplayer/, retrieved November 16, 2016.

Only One Neuron either N-bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 67 - 72, 26.12.2016
https://doi.org/10.18201/ijisae.267039

Öz

In this
study, solutions to machine learning problems such as Monk’s 2 (M2),
Balloon and Tic-Tac-Toe problems employing a single neuron dependent on rules
which use either modified translated multiplicative (πm) neuron or
McCulloch-Pitts neuron model is proposed. Since M2 problem is
similar to N-bit parity problem, translated multiplicative (πt)
neuron model is modified for M2 problem. Also, McCulloch-Pitts
neuron model is used to increase classification performance. Then either πm or
McCulloch-Pitts neuron model is applied to Balloon and Tic-Tac-Toe problems.
When the result of proposed only one πm neuron model that is not
required any training stage and hidden layer is compared with the other
approaches, it shows satisfactory performance.




Kaynakça

  • [1] Iyoda, E. M., Nobuhara, H. and Hirota, K.: A Solution for the N-bit Parity Problem Using a Single Translated Multiplicative Neuron, Neural Processing Letters, vol.18, pp. 213-218, 2003.
  • [2] Arslanov, M.Z., Ashigaliev, D.U. and Ismail, E. E.: N-bit Parity Ordered Neural Network, Neurocomputing 48 (2002), 1053-1056
  • [3] Al-Rawi, M.: A Neural Network to Solve the Hybrid N-parity: Learning with Generalization Issues, Neurocomputing, vol.68, pp. 273-280, 2005
  • [4] Hohil, M. E., Liu, D., Smith, S. H., Solving the N-bit parity problem using neural networks, Neural Networks, vol.12, pp.1321-1323, 1999.
  • [5] Li, D., Hirasawa, K., Hu, J., Murata, J., Studying the effects on multiplication neurons for parity problem, 41st Society of Instrument and Control Engineers-SICE,2002.
  • [6] Kim, K., Kim, S., Joo, Y., Oh, A.S.: Enhanced fuzzy single layer perceptron, Advances in Neural Networks, vol.3496,pp. 603-608, 2005.
  • [7] Setino, R.: On the solution of the parity problem by a single hidden layer feedforward neural network, Neurocomputing, vol.16 (3), pp. 225-235, 1997.
  • [8] Setiono, R., Hui, L. C. K.: Some N-bit parity problems are solvable by feed-forward networks with less than n hidden units, Int. Joint Conf. on Neural Networks, 1993, pp. 305-308.
  • [9] Schmitt, M.: On the complexity of computing and learning with multiplicative neurons, Neural Computation, vol.14(2), pp. 241-301, 2002.
  • [10] Zhang, B.-T.: A Bayesian Evolutionary Approach to The Design and Learning of Heterogeneous Neural Trees, Integrated Computer-Aided Engineering, vol. 9(1), pp. 73-86, 2002.
  • [11] Bas, E., Uslu, V. R. and Egrioglu, E.: Robust learning algorithm for multiplicative neuron model artificial neural networks, Expert Systems with Applications, vol.56, pp. 80-88, 2016.
  • [12] Thrun, S.B., Bala, J., Bloedorn, E., Bratko, I., Cestnik, B., Cheng, J., De Jong, K., Dzeroski, S., Fahlman, S.E., Fisher, D., Hamann, R., Kaufman, K., Keller, S., Kononenko, I., Kreuziger, J., Michalski, R.S., Mitchell, T., Pachowicz, P., Reich, Y., Vafaie, H., Van de Welde, W., Wenzel, W., Wnek, J., and Zhang, J.: The Monk’s Problems: A Perfor. Comparison of Different Learning Algorithm, a Report, Carnegie Mellon University CMU-CS-91-197, 1991.
  • [13] University of California, Irvine Dataset: [Online]. Available: ftp://ftp.ics.uci.edu/pub/machine-learning-databases/monks-problems/, retrieved November 16 2016.
  • [14] Pilgrim, R., A., “Tic-Tac-Toe: Introduction Expert Systems to Middle School Students”, Acm Sigcse Bulletin, Vol. 27, 340–344, (1995).
  • [15] Gordon, A., “A General Algorithm for Tic-Tac-Toe Board Evaluation”, Journal of Computing Sciences in Colleges, Vol. 21, 42-46, (2006).
  • [16] Solorio, T. and Fuentes, O.: Taking Advantage of Unlabelled Data with the Ordered Classification Algorithm, ACTA, Proc. of AI and Soft Computing ASC 2002, 357-200, (2002).
  • [17] Noughts And Crosses - The oldest graphical computer game, http://www.pong-story.com/1952.htm, retrieved November 16, 2016.
  • [18] Wachsmuth, B. G., Tic-tac-toe game, http://pirate.shu.edu/~wachsmut/Teaching/CSAS1111/Assigns-CPP/assign7.html, retrieved November 17, 2016.
  • [19] Massey, B., Tic-tac-toe board evaluation, http://web.cecs.pdx.edu/~bart/cs541-fall2001/homework/3-learn.html, retrieved November 17, 2016.
  • [20] Appel, A. W., Game player programs, http://www.cs.princeton.edu/courses/archive/spr05/cos217/asgts/gameplayer/, retrieved November 16, 2016.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Research Article
Yazarlar

Ali Özdemir Bu kişi benim

Mehmet Melih İnal

Yayımlanma Tarihi 26 Aralık 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 4 Sayı: Special Issue-1

Kaynak Göster

APA Özdemir, A., & İnal, M. M. (2016). Only One Neuron either N-bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 67-72. https://doi.org/10.18201/ijisae.267039
AMA Özdemir A, İnal MM. Only One Neuron either N-bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems. International Journal of Intelligent Systems and Applications in Engineering. Aralık 2016;4(Special Issue-1):67-72. doi:10.18201/ijisae.267039
Chicago Özdemir, Ali, ve Mehmet Melih İnal. “Only One Neuron Either N-Bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems”. International Journal of Intelligent Systems and Applications in Engineering 4, sy. Special Issue-1 (Aralık 2016): 67-72. https://doi.org/10.18201/ijisae.267039.
EndNote Özdemir A, İnal MM (01 Aralık 2016) Only One Neuron either N-bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 67–72.
IEEE A. Özdemir ve M. M. İnal, “Only One Neuron either N-bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems”, International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. Special Issue-1, ss. 67–72, 2016, doi: 10.18201/ijisae.267039.
ISNAD Özdemir, Ali - İnal, Mehmet Melih. “Only One Neuron Either N-Bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (Aralık 2016), 67-72. https://doi.org/10.18201/ijisae.267039.
JAMA Özdemir A, İnal MM. Only One Neuron either N-bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:67–72.
MLA Özdemir, Ali ve Mehmet Melih İnal. “Only One Neuron Either N-Bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems”. International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. Special Issue-1, 2016, ss. 67-72, doi:10.18201/ijisae.267039.
Vancouver Özdemir A, İnal MM. Only One Neuron either N-bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):67-72.

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