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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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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