Qubit Neural Tree Network With Applications in Nonlinear System Modeling
This paper proposed a newly quantum-inspired Qubit neural tree network with improved Qubit neuron, cross-layer connections and distinct phase operation functions for each neurons. A hybrid evolutionary algorithm that combines the modified gene expression programming with particle swarm optimization...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8463464/ |
| _version_ | 1818331886287585280 |
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| author | Feng Qi Chao Chen |
| author_facet | Feng Qi Chao Chen |
| author_sort | Feng Qi |
| collection | DOAJ |
| description | This paper proposed a newly quantum-inspired Qubit neural tree network with improved Qubit neuron, cross-layer connections and distinct phase operation functions for each neurons. A hybrid evolutionary algorithm that combines the modified gene expression programming with particle swarm optimization is also introduced to obtain the optimal structure with related parameters of the Qubit neural tree network. Three nonlinear system modeling problems are selected to evaluate the effectiveness and performance of the proposed model. The simulation results indicate that the Qubit neural tree network has better nonlinear mapping and generalization ability than related methods do. |
| first_indexed | 2024-12-13T13:26:58Z |
| format | Article |
| id | doaj.art-e2bb88999001461ca41d6408896a8094 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:26:58Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e2bb88999001461ca41d6408896a80942022-12-21T23:44:17ZengIEEEIEEE Access2169-35362018-01-016515985160610.1109/ACCESS.2018.28698948463464Qubit Neural Tree Network With Applications in Nonlinear System ModelingFeng Qi0https://orcid.org/0000-0002-7290-5066Chao Chen1School of Management Science and Engineering, Shandong Normal University, Shandong, ChinaSchool of Management Science and Engineering, Shandong Normal University, Shandong, ChinaThis paper proposed a newly quantum-inspired Qubit neural tree network with improved Qubit neuron, cross-layer connections and distinct phase operation functions for each neurons. A hybrid evolutionary algorithm that combines the modified gene expression programming with particle swarm optimization is also introduced to obtain the optimal structure with related parameters of the Qubit neural tree network. Three nonlinear system modeling problems are selected to evaluate the effectiveness and performance of the proposed model. The simulation results indicate that the Qubit neural tree network has better nonlinear mapping and generalization ability than related methods do.https://ieeexplore.ieee.org/document/8463464/Gene expression programmingnonlinear systemQubit neuronQubit neural tree network |
| spellingShingle | Feng Qi Chao Chen Qubit Neural Tree Network With Applications in Nonlinear System Modeling IEEE Access Gene expression programming nonlinear system Qubit neuron Qubit neural tree network |
| title | Qubit Neural Tree Network With Applications in Nonlinear System Modeling |
| title_full | Qubit Neural Tree Network With Applications in Nonlinear System Modeling |
| title_fullStr | Qubit Neural Tree Network With Applications in Nonlinear System Modeling |
| title_full_unstemmed | Qubit Neural Tree Network With Applications in Nonlinear System Modeling |
| title_short | Qubit Neural Tree Network With Applications in Nonlinear System Modeling |
| title_sort | qubit neural tree network with applications in nonlinear system modeling |
| topic | Gene expression programming nonlinear system Qubit neuron Qubit neural tree network |
| url | https://ieeexplore.ieee.org/document/8463464/ |
| work_keys_str_mv | AT fengqi qubitneuraltreenetworkwithapplicationsinnonlinearsystemmodeling AT chaochen qubitneuraltreenetworkwithapplicationsinnonlinearsystemmodeling |