Abstract
Shuffled frog leaping algorithm (SFLA) is a meta-heuristic optimization method that mimics the memetic evolution of a group of frogs in nature seeking for food, which has been very successful in a wide variety of optimization problems. A hybrid optimization method is proposed for self-tuning pulse coupled neural network (PCNN) parameters, a biologically inspired spiking neural network, based on SFLA and was used to detect rotary kiln infrared image edges automatically and successfully. The effective of the proposed method is verified by simulation results, that is to say, the quality of the rotary kiln grayscale image edge detection is much better and parameters are set automatically.
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Ziou, D., Tabbone, S.A.: Multi-scale Edge Detector. Pattern Recognition 26, 1305–1314 (1993)
Johnson, J.L., Padgett, M.L.: PCNN Models and Applications. IEEE Trans. on Neural Networks 10, 480–498 (1999)
Kuntimad, G., Ranganath, H.S.: Perfect Image Segmentation Using Pulse Coupled Neural Networks. IEEE Trans. on Neural Networks 10, 591–598 (1999)
Ma, Y.D., Dai, R.L., Li, L.: Automated Image Segmentation Using Pulse Coupled Neural Networks and Images Entropy. Journal of China Institute of Communications 23, 46–51 (2002)
Liu, Q., Ma, Y.D., Qian, Z.B.: Automated Image Segmentation Using Improved PCNN Model Based on Cross-entropy. Journal of Image and Graphics 10, 579–584 (2005)
Ma, Y.D., Qi, C.L.: Study of Automated PCNN System Based on Genetic Algorithm. Journal of System Simulation 18, 722–725 (2006)
Wang, J.S., Cong, F.W.: Grayscale Image Edge Detection Based on Pulse-coupled Neural Network and Particle Swarm Optimization. In: 20th Chinese Control and Decision Conference, pp. 2492–2495. IEEE Press, New York (2008)
Eusuff, M.M., Lansey, K.E.: Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm. Journal of Water Resources Planning and Management 129, 210–225 (2003)
Elbeltagi, E., Hezagy, T., Grierson, D.: Comparison Among Five Evolutionary-based Optimization Algorithms. Advanced Engineering Informatics 19, 43–53 (2005)
Rahimi-Vahed, A., Mirzaei, A.H.: A Hybrid Multi-objective Shuffled Frog-leaping Algorithm for a Mixed-model Assembly Line Sequencing Problem. Computers and Industrial Engineering 53, 642–666 (2007)
Rahimi-Vahed, A., Dangchi, M., Rafiei, H.: A Novel Hybrid Multi-objective Shuffled Frog-leaping Algorithm for a Bi-criteria Permutation Flow Shop Scheduling Problem. International Journal of Advanced Manufacturing Technology 41, 1227–1239 (2009)
Amiri, B., Fathian, M., Maroosi, A.: Application of Shuffled Frog-leaping Algorithm on Clustering. International Journal of Advanced Manufacturing Technology 45, 199–209 (2009)
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Wang, Js., Zhang, Y. (2011). Research on Edge Detection Algorithm of Rotary Kiln Infrared Color Image. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_45
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DOI: https://doi.org/10.1007/978-3-642-23896-3_45
Publisher Name: Springer, Berlin, Heidelberg
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