Coke Ratio Prediction Based on Immune Particle Swarm Neural Networks Article Swipe
Kai Yang
,
Yonglong Jin
,
Zhijun He
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.2174/1874110x01509011576
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.2174/1874110x01509011576
The clonal selection mechanism and vaccination strategy of immune system are introduced into particle swarm optimization algorithm in this paper, in order to enhance the ability of global exploration of PSO, avoiding getting into local optimum and improving the accuracy and convergence speed of BP networks.The global Cauchy mutation operator and local Gauss mutation operator are used to improve the ability of searching global optimization and the accuracy of local optimization.Then the weights and thresholds of neural networks are trained by applying the immune particle swarm optimization.Finally the coke ratio forecasting model is established based on the modified BP neural networks optimized by immune particle swarm optimizer.The result shows the forecast accuracy is more accurate than both the BP neural networks optimized by the standard PSO and the traditional BP neural networks, and provides an effective way to reduce the coke ratio and achieve energy conservation and emission reduction for iron and steel enterprise.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2174/1874110x01509011576
- http://benthamopen.com/contents/pdf/TOCSJ/TOCSJ-9-1576.pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2232862754
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2232862754Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2174/1874110x01509011576Digital Object Identifier
- Title
-
Coke Ratio Prediction Based on Immune Particle Swarm Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-10-01Full publication date if available
- Authors
-
Kai Yang, Yonglong Jin, Zhijun HeList of authors in order
- Landing page
-
https://doi.org/10.2174/1874110x01509011576Publisher landing page
- PDF URL
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https://benthamopen.com/contents/pdf/TOCSJ/TOCSJ-9-1576.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://benthamopen.com/contents/pdf/TOCSJ/TOCSJ-9-1576.pdfDirect OA link when available
- Concepts
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Artificial neural network, Particle (ecology), Computer science, Coke, Particle swarm optimization, Artificial intelligence, Machine learning, Materials science, Biology, Metallurgy, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2018: 1Per-year citation counts (last 5 years)
- References (count)
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8Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.conservation | 145 |
| abstract_inverted_index.networks.The | 45 |
| abstract_inverted_index.optimization | 15, 64 |
| abstract_inverted_index.optimizer.The | 106 |
| abstract_inverted_index.optimization.Then | 70 |
| abstract_inverted_index.optimization.Finally | 86 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.67097407 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |