Training an Approximate Logic Dendritic Neuron Model Using Social Learning Particle Swarm Optimization Algorithm
With the rapid development of artificial neural networks, recent studies have shown that dendrites play a vital role in neural computations. In this study, we propose a dendritic neuron model called the approximate logic dendritic neuron model (ALDNM) to solve classification problems. The ALDNM can...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8853321/ |
| _version_ | 1819174531941531648 |
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| author | Shuangyu Song Xingqian Chen Cheng Tang Shuangbao Song Zheng Tang Yuki Todo |
| author_facet | Shuangyu Song Xingqian Chen Cheng Tang Shuangbao Song Zheng Tang Yuki Todo |
| author_sort | Shuangyu Song |
| collection | DOAJ |
| description | With the rapid development of artificial neural networks, recent studies have shown that dendrites play a vital role in neural computations. In this study, we propose a dendritic neuron model called the approximate logic dendritic neuron model (ALDNM) to solve classification problems. The ALDNM can be divided into four layers: the synaptic layer, the dendritic layer, the membrane layer, and the soma body. Considering the limitation of the back-propagation (BP) algorithm, we employ a heuristic optimization called the social learning particle swarm optimization algorithm (SL-PSO) to train the ALDNM. In order to investigate the effectiveness of SL-PSO for training the ALDNM, we compare this training method with BP and four other typical heuristic optimization methods. Moreover, the proposed ALDNM is also compared with seven classifiers to verify its performance. The experimental results and statistical analysis on four classification problems indicate that the proposed ALDNM trained by SL-PSO can provide a competitive performance for solving the classification problems. It is worth emphasizing that the structure of the trained ALDNM can be greatly simplified owing to the unique pruning operations. Furthermore, the simplified ALDNM for a specific problem can be converted into a corresponding logic circuit classifier for a fast classification. |
| first_indexed | 2024-12-22T20:40:28Z |
| format | Article |
| id | doaj.art-eec31a966aaf42cc8624e62e1f2a81fc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:40:28Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eec31a966aaf42cc8624e62e1f2a81fc2022-12-21T18:13:21ZengIEEEIEEE Access2169-35362019-01-01714194714195910.1109/ACCESS.2019.29446828853321Training an Approximate Logic Dendritic Neuron Model Using Social Learning Particle Swarm Optimization AlgorithmShuangyu Song0https://orcid.org/0000-0002-4025-2638Xingqian Chen1https://orcid.org/0000-0002-1188-7917Cheng Tang2https://orcid.org/0000-0002-8148-1509Shuangbao Song3https://orcid.org/0000-0001-7562-6698Zheng Tang4Yuki Todo5Faculty of Engineering, University of Toyama, Toyama, JapanFaculty of Engineering, University of Toyama, Toyama, JapanFaculty of Engineering, University of Toyama, Toyama, JapanFaculty of Engineering, University of Toyama, Toyama, JapanFaculty of Engineering, University of Toyama, Toyama, JapanFaculty of Electrical and Computer Engineering, Kanazawa University, Kanazawa, JapanWith the rapid development of artificial neural networks, recent studies have shown that dendrites play a vital role in neural computations. In this study, we propose a dendritic neuron model called the approximate logic dendritic neuron model (ALDNM) to solve classification problems. The ALDNM can be divided into four layers: the synaptic layer, the dendritic layer, the membrane layer, and the soma body. Considering the limitation of the back-propagation (BP) algorithm, we employ a heuristic optimization called the social learning particle swarm optimization algorithm (SL-PSO) to train the ALDNM. In order to investigate the effectiveness of SL-PSO for training the ALDNM, we compare this training method with BP and four other typical heuristic optimization methods. Moreover, the proposed ALDNM is also compared with seven classifiers to verify its performance. The experimental results and statistical analysis on four classification problems indicate that the proposed ALDNM trained by SL-PSO can provide a competitive performance for solving the classification problems. It is worth emphasizing that the structure of the trained ALDNM can be greatly simplified owing to the unique pruning operations. Furthermore, the simplified ALDNM for a specific problem can be converted into a corresponding logic circuit classifier for a fast classification.https://ieeexplore.ieee.org/document/8853321/dendritic neuron modelheuristic optimizationclassificationpruninglogic circuit |
| spellingShingle | Shuangyu Song Xingqian Chen Cheng Tang Shuangbao Song Zheng Tang Yuki Todo Training an Approximate Logic Dendritic Neuron Model Using Social Learning Particle Swarm Optimization Algorithm IEEE Access dendritic neuron model heuristic optimization classification pruning logic circuit |
| title | Training an Approximate Logic Dendritic Neuron Model Using Social Learning Particle Swarm Optimization Algorithm |
| title_full | Training an Approximate Logic Dendritic Neuron Model Using Social Learning Particle Swarm Optimization Algorithm |
| title_fullStr | Training an Approximate Logic Dendritic Neuron Model Using Social Learning Particle Swarm Optimization Algorithm |
| title_full_unstemmed | Training an Approximate Logic Dendritic Neuron Model Using Social Learning Particle Swarm Optimization Algorithm |
| title_short | Training an Approximate Logic Dendritic Neuron Model Using Social Learning Particle Swarm Optimization Algorithm |
| title_sort | training an approximate logic dendritic neuron model using social learning particle swarm optimization algorithm |
| topic | dendritic neuron model heuristic optimization classification pruning logic circuit |
| url | https://ieeexplore.ieee.org/document/8853321/ |
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