References
- [1] R.N. Yadav, P.K. Kalra, J. John, Time series prediction with single multiplicative neuron model, Applied Soft Computing, 7, 2007, 1157-1163.
- [2] E. Egrioglu, C.H. Aladag, U. Yolcu, and E. Bas, Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting, Neural Processing Letters 41(2), 2015, 249-258.
- [3] O. Gundogdu, E. Egrioglu, C.H. Aladag, and U. Yolcu, Multiplicative neuron model artificial neural network based on gauss activation function, Neural Computing and Applications 27(4), 2015, 927-935
- [4] D.E. Rumelhart, and J.L. Mcclelland, Parallel distributed processing: explorations in the microstructure of cognition, Cambridge (Britian): MIT Press, 1986.
- [5] C.L. Giles, and T. Maxwell, Learning, invariance, and generalization in a high-order neural network, Appl Opt, 26(23), 1978, 4972–8.
- [6] R. Durbin, and D.E. Rumelhart, Product units: a computationally powerful and biologically plausible extension to back propagation networks, Neural Computation, 1, 1989:133–42.
- [7] Y. Shin, and J. Gosh, The Pi-sigma Network: An efficient higher-order neural network for pattern classification and function approximation. In Proceedings of the International Joint Conference on Neural Networks, 1991.
- [8] R. Ghazali. A. Husaini, L.H. Ismail, T. Herawan, and Y.M. Hassim, The performance of a recurrent HONN for temperature time series prediction, 2014 International Joint Conference on Neural Networks (IJCNN), July 6-11, Proceeding Book, page 518-524, Beijing, China, 2014.
- [9] R. Ghazali. A. Husaini, and W. El-Deredy, Application of ridge polynomial neural networks to financial time series prediction. In: 2006 International joint conference on neural networks; July, 16–21, 2006, 913–20.
- [10] R. Ghazali, A.J. Hussain, P. Liatsis, and H. Tawfik, The application of ridge polynomial neural network to multi-step ahead financial time series prediction, Neural Computing & Applications, 17(3), 2008, 311–323.
- [11] H. Tawfik, and P. Liatsis, Prediction of non-linear time-series using higher-order neural networks, Proceeding IWSSIP’97 Conference, Poznan, Poland, 1977.
- [12] N. Yong, and D. Wei, A hybrid genetic learning algorithm for Pi– sigma neural network and the analysis of its convergence, In: IEEE fourth international conference on natural computation, 19–23, 2008
- [13] J. Nayak, B. Naik, and H.S. Behera, A hybrid PSO-GA based Pi sigma neural network (PSNN) with standard back propagation gradient descent learning for classification. International Conference on Control, Instrumentation, Communication and Computational Technologies, ICCICCT 2014, art. no. 6993082, 878-885, 2014b.
- [14] J. Nayak, B. Naik, and H.S. Behera, and A. Abraham, Particle swarm optimization based higher order neural network for classification, Smart Innovation, Systems and Technologies, 31, 2015, 401-414.
- [15] L. Chien-Kuo, Memory-based Sigma–Pi–Sigma neural network, IEEE SMC, TP1F5; 2002, 112–8.
- [16] A.J. Hussain, and P. Liatsis, Recurrent Pi–Sigma networks for DPCM image coding, Neurocomputing, 55, 2002, 363–82.
- [17] J. Nayak, D.P. Kanungo, B. Naik, and H.S. Behera, A higher order evolutionary Jordan Pi-sigma neural network with gradient descent learning for classification, 2014 International Conference on High Performance Computing and Applications, ICHPCA 2014, Article number 7045328.
- [18] J. Kennedy, R. Eberhart, Particle swarm optimization, In Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, USA, IEEE Press., 1995, 1942–1948.
- [19] C.H. Aladag, U. Yolcu, and E. Egrioglu, A new multiplicative seasonal neural network model based on particle swarm optimization, Neural Processing Letters 37(3), 2013, 251-262.10.1007/s11063-012-9244-y
- [20] G. Janacek, Practical time series. Oxford University Press Inc., New York, 156, 2001.
- [21] U. Yolcu, E. Egrioglu, C.H. Aladag, A new linear & nonlinear artificial neural network model for time series forecasting, Decision Support Systems, 2013, 1340–1347.10.1016/j.dss.2012.12.006
- [22] J.L. Elman, Finding structure in time, Cognitive Science, 14 (2), 1990, 179–211.10.1207/s15516709cog1402_1