The impact of VMAX activation function in particle swarm optimization neural network
Back propagation (BP) Network is the most common technique in Artificial Neural Network (ANN) learning. However, major disadvantages of BP are its convergence rate is relatively slow and always being trapped at the local minima. Therefore, latest optimization technique, Particle Swarm Optimization (...
保存先:
| 第一著者: | |
|---|---|
| フォーマット: | 学位論文 |
| 言語: | English |
| 出版事項: |
2008
|
| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/9456/1/LeeYiewSiangFSKSM2008.pdf http://eprints.utm.my/id/eprint/9456/ |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| id |
my.utm.9456 |
|---|---|
| record_format |
eprints |
| spelling |
my.utm.94562018-07-19T01:38:53Z http://eprints.utm.my/id/eprint/9456/ The impact of VMAX activation function in particle swarm optimization neural network Lee, Yiew Siang QA75 Electronic computers. Computer science Back propagation (BP) Network is the most common technique in Artificial Neural Network (ANN) learning. However, major disadvantages of BP are its convergence rate is relatively slow and always being trapped at the local minima. Therefore, latest optimization technique, Particle Swarm Optimization (PSO) is chosen and applied in feed forward neural network to enhance the network learning. In conventional PSO, maximum velocity, Vmax serves as a constraint that controls the maximum global exploration ability PSO can have. By setting a too small maximum velocity, maximum global exploration ability is limited and PSO will always favor a local search no matter what the inertia weight is. By setting a large maximum velocity, PSO can have a large range of exploration ability. Therefore, in this study, different activation functions will apply in the PSO Vmax function in order to control global exploration of particles and increase the convergence rate as well as correct classification. The preliminary results show that Vmax hyperbolic tangent function give promising results in term of convergence rate and classification compared to Vmax sigmoid function and standard Vmax function. 2008-06 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/9456/1/LeeYiewSiangFSKSM2008.pdf Lee, Yiew Siang (2008) The impact of VMAX activation function in particle swarm optimization neural network. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems. |
| institution |
Universiti Teknologi Malaysia |
| building |
UTM Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Malaysia |
| content_source |
UTM Institutional Repository |
| url_provider |
http://eprints.utm.my/ |
| language |
English |
| topic |
QA75 Electronic computers. Computer science |
| spellingShingle |
QA75 Electronic computers. Computer science Lee, Yiew Siang The impact of VMAX activation function in particle swarm optimization neural network |
| description |
Back propagation (BP) Network is the most common technique in Artificial Neural Network (ANN) learning. However, major disadvantages of BP are its convergence rate is relatively slow and always being trapped at the local minima. Therefore, latest optimization technique, Particle Swarm Optimization (PSO) is chosen and applied in feed forward neural network to enhance the network learning. In conventional PSO, maximum velocity, Vmax serves as a constraint that controls the maximum global exploration ability PSO can have. By setting a too small maximum velocity, maximum global exploration ability is limited and PSO will always favor a local search no matter what the inertia weight is. By setting a large maximum velocity, PSO can have a large range of exploration ability. Therefore, in this study, different activation functions will apply in the PSO Vmax function in order to control global exploration of particles and increase the convergence rate as well as correct classification. The preliminary results show that Vmax hyperbolic tangent function give promising results in term of convergence rate and classification compared to Vmax sigmoid function and standard Vmax function. |
| format |
Thesis |
| author |
Lee, Yiew Siang |
| author_facet |
Lee, Yiew Siang |
| author_sort |
Lee, Yiew Siang |
| title |
The impact of VMAX activation function in particle swarm optimization neural network |
| title_short |
The impact of VMAX activation function in particle swarm optimization neural network |
| title_full |
The impact of VMAX activation function in particle swarm optimization neural network |
| title_fullStr |
The impact of VMAX activation function in particle swarm optimization neural network |
| title_full_unstemmed |
The impact of VMAX activation function in particle swarm optimization neural network |
| title_sort |
impact of vmax activation function in particle swarm optimization neural network |
| publishDate |
2008 |
| url |
http://eprints.utm.my/id/eprint/9456/1/LeeYiewSiangFSKSM2008.pdf http://eprints.utm.my/id/eprint/9456/ |
| _version_ |
1643645160071888896 |
| score |
13.251813 |