The conventional fuzzy density values of the fuzzy integral were decided by heuristic experiments.
In this paper, particle swarm optimization (PSO) was used to adaptively find optimal fuzzy density values.
To combine the advantages of each CNN type, the evaluation of each CNN type in EFI-CNNs is necessary.
Three CNN structures, Alex Net, very deep convolutional neural network (VGG16), and Goog Le Net, and three databases, computational intelligence application laboratory (CIA), Morph, and cross-age celebrity dataset (CACD2000), were used in experiments to classify age and gender.
The trained CNNs’ outputs were set as inputs of a fuzzy integral.
The classification results were operated using either Sugeno or Choquet output rules.
To make the most of these advantages, evolutionary-fuzzy-integral-based convolutional neural networks (EFI-CNNs) are proposed in this paper.
The proposed EFI-CNNs were verified by way of face classification of age and gender.
Early-termination techniques for a belief-propagation (BP) decoder of polar codes can improve the decoding throughput by finishing a decoding iteration when an early-termination condition is satisfied.
In the BP decoders, the early-termination condition plays an important role, as it affects decoding iteration [...] Read more.