Application of Neuro-Fuzzy Inference System on Wood Identification

Abstract:

Article Preview

Searching for systems with intelligent, flexible, and self-adjusting solutions on imaging, which could provide the contraction of the human operators’ presence, a range of techniques is found. Each one of them can control the process through the assistance of autonomous systems, either software or hardware. Therefore, modeling by traditional computational techniques is quite difficult, considering the complexity and non-linearity of image systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) behaves well as noise elimination and non-linear data treatment. Consequently, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, therefore, add value to the final product. Additionally, the Artificial Intelligence techniques, such as Neuro-Fuzzy Networks (NFN), have shown efficient, since they combine the ability to learn from examples and to generalize the learned information from the ANNs with the capacity of Fuzzy Logic, in order to transform linguistic variables in rules. Then, ANFIS plays active roles in an effort to reach a specific goal.

Info:

Periodical:

Edited by:

Kai Li Zhang

Pages:

667-671

Citation:

F. H. A. Vieira et al., "Application of Neuro-Fuzzy Inference System on Wood Identification", Applied Mechanics and Materials, Vol. 590, pp. 667-671, 2014

Online since:

June 2014

Export:

Price:

$38.00

* - Corresponding Author

[1] W. S. McCuloch, W. H. Pitts W H, A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, vol. 5, (1943) pp.115-133.

DOI: https://doi.org/10.1007/bf02478259

[2] D. O. Hebb, The Organization of Behavior: Neuropsychological Theory, N. Y.: Willey, (1949).

[3] M. Rosenblatt, The Perceptron: A probabilistic model for information storage and organization in the Brain. Psychological review, vol. 65, n. 6, (1958) pp.386-408.

DOI: https://doi.org/10.1037/h0042519

[4] S. Haykin, Neural Networks: A Comprehensive Foundation. New York: Willey & Sons, (2001).

[5] K. Rawat, K. BURSE, Feature Selection and Classification of Premalignant Pancreatic Cancer Using Gentic-ANFIS Methodology. Oriental Int'l Journal of Innovative Engineering. Vol. 2 (2013).

[6] M. J. P. Castanho, F. Hernandes, A. M. De Ré, S. Rautenberg, A. Billis, Fuzzy expert system for predicting pathological stage of prostate cancer. Expert Systems. (2013) p.466–470.

DOI: https://doi.org/10.1016/j.eswa.2012.07.046

[7] S. K. Halgamude, M. Glesner, Neural networks in designing Fuzzy systems for real world applications, Fuzzy Sets and Systems, (1994).

DOI: https://doi.org/10.1016/0165-0114(94)90242-9

[8] F. Gomide, M. Figueiredo, W. Pedrycz, A neural Fuzzy network: Structure and learning, Fuzzy Logic and Its Applications, ISIS, Kluwer Academic Publishers, Netherlands, (1998) pp.177-186.

DOI: https://doi.org/10.1007/978-94-009-0125-4_17

[9] S. O. Resende, Sistemas Inteligentes: Fundamentos e Aplicações. Ed. Malone, (2005).

[10] L. Rutkowski, Computational Intelligence – Techniques. Berlin, Heidel-berg: Springer, (2008).

[11] C. Affonso, R. J. Sassi, A Rough-Neuro Fuzzy Network Applied to Polymer Processing, IEEE Conference, pp.355-361, Cheng du-China (2010).

[12] E. Mizutani, Coactive neural fuzzy modeling. IEEE Trans. Neural Networks, vol. 2,  (1995) pp.720-725.

[13] R. C. Gonzales, R. E. Woods, Digital Image Processing. 3rd. Ed. NJ: Prentice Hall, (2008).

[14] O. N. A. Al-Allaf, S. A. Abdalkader, A. A. Tamimi, Pattern Recognition Neural Network for Improving the Performance of Iris Recognition System. Int'l Journal of Scientific & Engineering Research, Volume 4, Issue 6, Jun, (2013) pp.661-667.

[15] B. Hamid, R. M. Meybodi, A learning automata-based algorithm for three-layer neural networks. Int'l Journal of Systems Science, Vol. 40, No. 1, Jan, (2009) p.101–118.

[16] R. Young, An efficient approach to converting 3D image data into highly accurate computational models. Philosophical Transactions of the Royal Society 366, (2008) p.3155–3173.

DOI: https://doi.org/10.1098/rsta.2008.0090

Fetching data from Crossref.
This may take some time to load.