Comparison of particle swarm optimization variants with fuzzy dynamic parameter adaptation for modular granular neural networks for human recognition

In this paper dynamic parameter adjustment in particle swarm optimization (PSO) for modular neural network (MNN) design using granular computing and fuzzy logic (FL) is proposed. Nowadays, there are a plethora of optimization techniques, but their implementations require having knowledge about these techniques in order to establish their parameters, because the performance and final results of a particular technique depend on the optimal parameter values. For this reason, in this paper the fuzzy adjustment of parameters during the execution is proposed, and this proposal allows to adjust the parameters depending on current PSO behavior in each iteration. The proposed method performs modular neural network optimization applied to human recognition using benchmark ear, iris and face databases. Two fuzzy inference systems are proposed to perform this dynamic adjustment, comparisons against a PSO without this dynamic adjustment (simple PSO) are performed to verify if the proposed adjustment using a fuzzy system is better improving recognition rate and execution time. The PSO variants presented in this paper are aimed at performing MNNs optimization. This optimization consists on finding optimal parameters, such as: the number of modules (or sub granules), percentage of data for the training phase, learning algorithm, goal error, number of hidden layers and their number of neurons.