Functional link neural network with modified bee-firefly learning algorithm for classification task
Classification is one of the most frequent studies in the area of Artificial Neural Network (ANNs). The ANNs are capable of generating a complex mapping between the input and the output space to form arbitrarily complex nonlinear decision boundaries. One of the best-known types of ANNs is the Multil...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
2016
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/9129/ http://eprints.uthm.edu.my/9129/1/YANA_MAZWIN_MOHMAD_HASSIM.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Classification is one of the most frequent studies in the area of Artificial Neural
Network (ANNs). The ANNs are capable of generating a complex mapping between
the input and the output space to form arbitrarily complex nonlinear decision
boundaries. One of the best-known types of ANNs is the Multilayer Perceptron
(MLP). MLP usually requires a large amount of available measures in order to
achieve good classification accuracy. To overcome this, a Functional Link Neural
Networks (FLNN) which has a single layer of trainable connection weights is used.
The single layer property of FLNN also make the learning algorithm used less
complicated compared to MLP network. The standard learning method for tuning
weights in FLNN is Backpropagation (BP) learning algorithm. However, the
algorithm is prone to get trapped in local minima which affect the performance of
FLNN network. This work proposed the implementation of modified Artificial Bee
Colony with Firefly algorithm for training the FLNN network to overcome the
drawback of BP-learning algorithm. The aim is to introduce an improved learning
algorithm that can provide a better solution for training the FLNN network for the
task of classification. |
|---|