Optimal Pixel-to-Shift Standard Deviation Ratio for Training 2-Layer Perceptron on Shifted 60 × 80 Images with Pixel Distortion in Classifying Shifting-Distorted Objects
References
- [1] K. Fukushima and S. Miyake, “Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position,”, vol. 15, iss. 6, 1982, pp. 455–469.
- [2] K. Fukushima, “Self-organization of shift-invariant receptive fields,”, vol. 12, iss. 6, 1999, pp. 791–801.
- [3] V. V. Romanuke, “An attempt for 2-layer perceptron high performance in classifying shifted monochrome 60-by-80-images via training with pixel-distorted shifted images on the pattern of 26 alphabet letters,”, no. 2, 2013, pp. 112–118.
- [4] K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,”, vol. 36, iss. 4, 1980, pp. 193–202.
- [5] K. Fukushima, “Training multi-layered neural network neocognitron,”, vol. 40, 2013, pp. 18–31.
- [6] G. Arulampalam and A. Bouzerdoum, “A generalized feedforward neural network architecture for classification and regression,”, vol. 16, iss. 5–6, 2003, pp. 561–568.
- [7] C. Yu, M. T. Manry, J. Li, and P. L. Narasimha, “An efficient hidden layer training method for the multilayer perceptron,”, vol. 70, iss. 1–3, 2006, pp. 525–535.
- [8] V. V. Romanuke, “Setting the hidden layer neuron number in feedforward neural network for an image recognition problem under Gaussian noise of distortion,”, vol. 6, no. 2, 2013, pp. 38–54.
- [9] V. V. Romanuke, “Dependence of performance of feed-forward neuronet with single hidden layer of neurons against its training smoothness on noised replicas of pattern alphabet,” Bulletin of Khmelnitskiy National University., no. 1, 2013, pp. 201–206.
- [10] V. V. Romanuke, “A 2-layer perceptron performance improvement in classifying 26 turned monochrome 60-by-80-images via training with pixel-distorted turned images,”, no. 5, 2014, pp. 55–62.
- [11] V. V. Romanuke, “Classification error percentage decrement of two-layer perceptron for classifying scaled objects on the pattern of monochrome 60-by-80-images of 26 alphabet letters by training with pixel-distorted scaled images,” Scientific bulletin of Chernivtsi National University of Yuriy Fedkovych., vol. 4, iss. 3, 2013, pp. 53–64.
- [12] P. A. Castillo, J. J. Merelo, M. G. Arenas, and G. Romero, “Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters,”, vol. 177, iss. 14, 2007, pp. 2884–2905.
- [13] S. S. Malalur, M. T. Manry, and P. Jesudhas, “Multiple optimal learning factors for the multi-layer perceptron,”, vol. 149, p. C, 2015, pp. 1490–1501.
- [14] M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,”, vol. 5, iss. 6, 1994, pp. 989–993.
- [15] T. Kathirvalavakumar and S. Jeyaseeli Subavathi, “Neighborhood based modified backpropagation algorithm using adaptive learning parameters for training feedforward neural networks,”, vol. 72, iss. 16–18, 2009, pp. 3915–3921.
- [16] V. V. Romanuke, “Accuracy improvement in wear state discontinuous tracking model regarding statistical data inaccuracies and shifts with boosting mini-ensemble of two-layer perceptrons,”, no. 4, 2014, pp. 55–58.
- [17] A. Nied, S. I. Jr. Seleme, G. G. Parma, and B. R. Menezes, “On-line neural training algorithm with sliding mode control and adaptive learning rate,”, vol. 70, iss. 16–18, 2007, pp. 2687–2691.
- [18] S. J. Yoo, J. B. Park, and Y. H. Choi, “Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates,” Information Sciences, vol. 177, iss. 15, 2007, pp. 3074–3098.
DOI: https://doi.org/10.1515/acss-2016-0008 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683 (formerly 2255-8691)
Language: English
Page range: 61 - 70
Published on: May 28, 2016
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
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© 2016 Vadim V. Romanuke, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.