Abstract
A long-term research goal of pulse-coupled neural network (PCNN) is to control neuronal firing states at each iteration. Recently, we propose a fire-controlled MSPCNN model (FC-MSPCNN) and provide a parameter setting method to control firing and fired neurons within an effective pulse cycle. We firstly design the proposed model according to previous prevalent PCNN models. Secondly, the setting methods of the adaptive parameters α, β, V, and Rn are given to control neuronal firing time more effectively. Thirdly, a predetermined parameter P will determine the total iteration times of all the neurons. Fourthly, we also propose a color image quantization method and a gallbladder image location method based on the FC-MSPCNN. The evaluation experiments achieve good image processing performances compared to prevalent PCNN models and prove the effectiveness and robustness of the proposed methods.
Published Version
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