An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
Learning an Artificial Neural Network (ANN) is an optimization task since it is desirable to find optimal weight sets of an ANN in the training process. Different equations are used to guide the network for providing an accurate result with less training and testing error. Most of the training al...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English English |
| Published: |
2014
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/1214/1/24p%20HABIB%20SHAH.pdf http://eprints.uthm.edu.my/1214/2/HABIB%20SHAH%20WATERMARK.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Learning an Artificial Neural Network (ANN) is an optimization task since it is
desirable to find optimal weight sets of an ANN in the training process. Different
equations are used to guide the network for providing an accurate result with less
training and testing error. Most of the training algorithms focus on weight values,
activation functions, and network structures for providing optimal outputs.
Backpropagation (BP) learning algorithm is the well-known learning technique that
trained ANN. However, some difficulties arise where the BP cannot get achievements
without trapping in local minima and converge very slow in the solution space.
Therefore, to overcome the trapping difficulties, slow convergence and difficulties in
finding optimal weight values, three improved Artificial Bee Colony (ABC) algorithms
built on the social insect behavior are proposed in this research for training ANN,
namely the widely used Multilayer Perceptron (MLP). Here, three improved learning
approaches inspired by artificial honey bee's behavior are used to train MLP. They are:
Global Guided Artificial Bee Colony (GGABC), Improved Gbest Guided Artificial Bee
Colony (IGGABC) and Artificial Smart Bee Colony (ASBC) algorithm. These improved
algorithms were used to increase the exploration, exploitation and keep them balance for
getting optimal results for a given task. Furthermore, here these algorithms used to train
the MLP on two tasks; the seismic event's prediction and Boolean function
classification. The simulation results of the MLP trained with improved algorithms were
compared with that when trained with the standard BP, ABC, Global ABC and Particle
Swarm Optimization algorithm. From the experimental analysis, the proposed improved
algorithms get better the classification efficacy for time series prediction and Boolean
function classification. Moreover, these improved algorithm's success to get high
accuracy and optimize the best network's weight values for training the MLP. |
|---|