Visible to the public Autoencoder Classification Algorithm Based on Swam Intelligence Optimization

TitleAutoencoder Classification Algorithm Based on Swam Intelligence Optimization
Publication TypeConference Paper
Year of Publication2018
AuthorsFeng, W., Chen, Z., Fu, Y.
Conference Name2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)
Date Publishedoct
Keywordsautoencoder classification algorithm, Autoencoder Neural Network, Backpropagation, BP algorithm, classification, Classification algorithms, composability, Decoding, Glass, Iris, neural nets, Neural networks, Neurons, particle swarm optimisation, particle swarm optimization, pattern classification, pubcrawl, QPSO, Quantum Particle Swarm Optimization, Quantum Particle Swarm Optimization(QPSO), softmax, Swam Intelligence Optimization, swarm intelligence
AbstractBP algorithm used by autoencoder classification algorithm. But the BP algorithm is not only complicated and inefficient, but sometimes falls into local optimum. This makes autoencoder classification algorithm are not very good. So in this paper we combie Quantum Particle Swarm Optimization (QPSO) and autoencoder classification algorithm. QPSO used to optimize the weight of autoencoder neural network and the parameter of softmax. This method has been tested on some database, and the experimental result shows that this method has got good results.
Citation Keyfeng_autoencoder_2018