Multi-Kernel Fusion for RBF Neural Networks Article Swipe
Syed Muhammad Atif
,
Shujaat Khan
,
Imran Naseem
,
Roberto Togneri
,
Mohammed Bennamoun
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.02592
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.02592
A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only a single kernel. In existing multi-kernel RBF algorithms, multi-kernel is formed by the convex combination of the base/primary kernels. In this paper, we propose a novel multi-kernel RBFNN in which every base kernel has its own (local) weight. This novel flexibility in the network provides better performance such as faster convergence rate, better local minima and resilience against stucking in poor local minima. These performance gains are achieved at a competitive computational complexity compared to the contemporary multi-kernel RBF algorithms. The proposed algorithm is thoroughly analysed for performance gain using mathematical and graphical illustrations and also evaluated on three different types of problems namely: (i) pattern classification, (ii) system identification and (iii) function approximation. Empirical results clearly show the superiority of the proposed algorithm compared to the existing state-of-the-art multi-kernel approaches.
Related Topics
Concepts
Kernel (algebra)
Radial basis function
Computer science
Maxima and minima
Artificial neural network
Radial basis function kernel
Convergence (economics)
Kernel method
Variable kernel density estimation
Artificial intelligence
Algorithm
Mathematical optimization
Pattern recognition (psychology)
Machine learning
Support vector machine
Mathematics
Economics
Economic growth
Combinatorics
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2007.02592
- https://arxiv.org/pdf/2007.02592
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287726937
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4287726937Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2007.02592Digital Object Identifier
- Title
-
Multi-Kernel Fusion for RBF Neural NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-06Full publication date if available
- Authors
-
Syed Muhammad Atif, Shujaat Khan, Imran Naseem, Roberto Togneri, Mohammed BennamounList of authors in order
- Landing page
-
https://arxiv.org/abs/2007.02592Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2007.02592Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2007.02592Direct OA link when available
- Concepts
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Kernel (algebra), Radial basis function, Computer science, Maxima and minima, Artificial neural network, Radial basis function kernel, Convergence (economics), Kernel method, Variable kernel density estimation, Artificial intelligence, Algorithm, Mathematical optimization, Pattern recognition (psychology), Machine learning, Support vector machine, Mathematics, Economics, Economic growth, Combinatorics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.neural | 10, 20, 29 |
| abstract_inverted_index.paper, | 69 |
| abstract_inverted_index.radial | 7, 26 |
| abstract_inverted_index.simple | 1 |
| abstract_inverted_index.single | 49 |
| abstract_inverted_index.system | 156 |
| abstract_inverted_index.(RBFNN) | 12 |
| abstract_inverted_index.(local) | 84 |
| abstract_inverted_index.against | 105 |
| abstract_inverted_index.amongst | 15 |
| abstract_inverted_index.clearly | 164 |
| abstract_inverted_index.current | 23 |
| abstract_inverted_index.kernel. | 50 |
| abstract_inverted_index.kernels | 35 |
| abstract_inverted_index.minima. | 110 |
| abstract_inverted_index.namely: | 151 |
| abstract_inverted_index.network | 30, 91 |
| abstract_inverted_index.pattern | 153 |
| abstract_inverted_index.popular | 18 |
| abstract_inverted_index.propose | 71 |
| abstract_inverted_index.provide | 37 |
| abstract_inverted_index.results | 163 |
| abstract_inverted_index.weight. | 85 |
| abstract_inverted_index.achieved | 115 |
| abstract_inverted_index.analysed | 133 |
| abstract_inverted_index.benefits | 40 |
| abstract_inverted_index.compared | 41, 121, 172 |
| abstract_inverted_index.equipped | 32 |
| abstract_inverted_index.existing | 52, 175 |
| abstract_inverted_index.function | 9, 28, 160 |
| abstract_inverted_index.kernels. | 66 |
| abstract_inverted_index.multiple | 34 |
| abstract_inverted_index.networks | 11 |
| abstract_inverted_index.previous | 44 |
| abstract_inverted_index.problems | 150 |
| abstract_inverted_index.proposed | 129, 170 |
| abstract_inverted_index.provides | 92 |
| abstract_inverted_index.stucking | 106 |
| abstract_inverted_index.Empirical | 162 |
| abstract_inverted_index.algorithm | 130, 171 |
| abstract_inverted_index.different | 147 |
| abstract_inverted_index.effective | 3 |
| abstract_inverted_index.evaluated | 144 |
| abstract_inverted_index.graphical | 140 |
| abstract_inverted_index.networks. | 21 |
| abstract_inverted_index.complexity | 120 |
| abstract_inverted_index.generation | 24, 45 |
| abstract_inverted_index.resilience | 104 |
| abstract_inverted_index.thoroughly | 132 |
| abstract_inverted_index.algorithms, | 55 |
| abstract_inverted_index.algorithms. | 127 |
| abstract_inverted_index.approaches. | 178 |
| abstract_inverted_index.combination | 62 |
| abstract_inverted_index.competitive | 118 |
| abstract_inverted_index.convergence | 98 |
| abstract_inverted_index.flexibility | 88 |
| abstract_inverted_index.performance | 39, 94, 112, 135 |
| abstract_inverted_index.significant | 38 |
| abstract_inverted_index.superiority | 167 |
| abstract_inverted_index.base/primary | 65 |
| abstract_inverted_index.contemporary | 124 |
| abstract_inverted_index.conventional | 19 |
| abstract_inverted_index.mathematical | 138 |
| abstract_inverted_index.multi-kernel | 53, 56, 74, 125, 177 |
| abstract_inverted_index.architectural | 4 |
| abstract_inverted_index.computational | 119 |
| abstract_inverted_index.illustrations | 141 |
| abstract_inverted_index.approximation. | 161 |
| abstract_inverted_index.identification | 157 |
| abstract_inverted_index.classification, | 154 |
| abstract_inverted_index.state-of-the-art | 176 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile |