6533b823fe1ef96bd127e003
RESEARCH PRODUCT
Extreme Learning Machines for Data Classification Tuning by Improved Bat Algorithm
Milan TubaAdis AlihodzicViktor TubaEva TubaDana Simiansubject
0209 industrial biotechnologyQuantitative Biology::Neurons and CognitionArtificial neural networkComputer sciencebusiness.industryData classificationProcess (computing)Approximation algorithm02 engineering and technologyMachine learningcomputer.software_genre020901 industrial engineering & automationGenetic algorithm0202 electrical engineering electronic engineering information engineeringBenchmark (computing)Feedforward neural network020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerBat algorithmdescription
Single hidden layer feed forward neural networks are widely used for various practical problems. However, the training process for determining synaptic weights of such neural networks can be computationally very expensive. In this paper we propose a new learning algorithm for learning the synaptic weights of the single hidden layer feedforward neural networks in order to reduce the learning time. We propose combining the upgraded bat algorithm with the extreme learning machine. The proposed approach reduces the number of evaluations needed to train a neural network and efficiently finds optimal input weights and the hidden biases. The proposed algorithm was tested on standard benchmark classification problems and functions and compared with other approaches from literature. The results have shown that our approach produces a satisfactory performance in almost all cases and that it can obtains solutions much faster than the traditional learning algorithms.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-07-01 | 2018 International Joint Conference on Neural Networks (IJCNN) |