Modified learning for discrete multi-valued neuron

Discrete Multi-valued Neuron (MVN) was proposed for solving classification problems. The neuron has an activation function which is used to create an output value for an input instance. The learning algorithm associated with discrete MVN was designed for multi-class classification. However, the algorithm can never converge for the cases of two-class classification. In this paper, we propose a revised activation function to overcome this difficulty. A concept of tolerating areas is included. Another scheme adopting new targets is also proposed to work with discrete MVN. Simulation results show that the proposed ideas can improve the performance of discrete MVN.

[1]  Anil K. Jain,et al.  A k-nearest neighbor artificial neural network classifier , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[2]  Kishan G. Mehrotra,et al.  Efficient classification for multiclass problems using modular neural networks , 1995, IEEE Trans. Neural Networks.

[3]  Naum N. Aizenberg,et al.  CNN based on multi-valued neuron as a model of associative memory for grey scale images , 1992, CNNA '92 Proceedings Second International Workshop on Cellular Neural Networks and Their Applications.

[4]  CHIH-WEN SHIH,et al.  Multistability in Recurrent Neural Networks , 2006, SIAM J. Appl. Math..

[5]  Edmundas Kazimieras Zavadskas,et al.  Multicategory Nets of Single-Layer Perceptrons: Complexity and Sample-Size Issues , 2010, IEEE Transactions on Neural Networks.

[6]  Igor N. Aizenberg.  Periodic Activation Function and a Modified Learning Algorithm for the Multivalued Neuron , 2010, IEEE Transactions on Neural Networks.

[7]  Neural Network: a Powerful Tool for Classification , 2007 .

[8]  C.A.L. Bailer-Jones,et al.  An introduction to artificial neural networks , 2001 .

[9]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[10]  Anna Hart,et al.  Using Neural Networks for Classification Tasks – Some Experiments on Datasets and Practical Advice , 1992 .

[11]  Jacek M. Zurada,et al.  Complex-valued multistate neural associative memory , 1996, IEEE Trans. Neural Networks.

[12]  Igor N. Aizenberg,et al.  Complex-Valued Neural Networks with Multi-Valued Neurons , 2011, Studies in Computational Intelligence.

[13]  Jacek M. Zurada,et al.  Blur Identification by Multilayer Neural Network Based on Multivalued Neurons , 2008, IEEE Transactions on Neural Networks.

[14]  Brian D. Ripley,et al.  Neural Networks and Related Methods for Classification , 1994 .