Neural Networks Applied in Chemistry. II. Neuro-Evolutionary Techniques in Process Modeling and Optimization
Artificial neural networks are widely used in data analysis and to control dynamic processes. These tools are powerful and versatile, but the way in which they are constructed, in particular their architecture, strongly affects their value and reliability. We review here some key techniques for opti...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
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2013
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| _version_ | 1797071856430219264 |
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| author | Cartwright, H Curteanu, S |
| author_facet | Cartwright, H Curteanu, S |
| author_sort | Cartwright, H |
| collection | OXFORD |
| description | Artificial neural networks are widely used in data analysis and to control dynamic processes. These tools are powerful and versatile, but the way in which they are constructed, in particular their architecture, strongly affects their value and reliability. We review here some key techniques for optimizing artificial neural networks and comment on their use in process modeling and optimization. Neuro-evolutionary techniques are described and compared, with the goal of providing efficient modeling methodologies which employ an optimal neural model. We also discuss how neural networks and evolutionary algorithms can be combined. Applications from chemical engineering illustrate the effectiveness and reliability of the hybrid neuro-evolutionary methods. © 2013 American Chemical Society. |
| first_indexed | 2024-03-06T22:59:17Z |
| format | Journal article |
| id | oxford-uuid:61837d81-57eb-442d-8dd2-631e6dd27ab1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:59:17Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:61837d81-57eb-442d-8dd2-631e6dd27ab12022-03-26T18:00:40ZNeural Networks Applied in Chemistry. II. Neuro-Evolutionary Techniques in Process Modeling and OptimizationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:61837d81-57eb-442d-8dd2-631e6dd27ab1EnglishSymplectic Elements at Oxford2013Cartwright, HCurteanu, SArtificial neural networks are widely used in data analysis and to control dynamic processes. These tools are powerful and versatile, but the way in which they are constructed, in particular their architecture, strongly affects their value and reliability. We review here some key techniques for optimizing artificial neural networks and comment on their use in process modeling and optimization. Neuro-evolutionary techniques are described and compared, with the goal of providing efficient modeling methodologies which employ an optimal neural model. We also discuss how neural networks and evolutionary algorithms can be combined. Applications from chemical engineering illustrate the effectiveness and reliability of the hybrid neuro-evolutionary methods. © 2013 American Chemical Society. |
| spellingShingle | Cartwright, H Curteanu, S Neural Networks Applied in Chemistry. II. Neuro-Evolutionary Techniques in Process Modeling and Optimization |
| title | Neural Networks Applied in Chemistry. II. Neuro-Evolutionary Techniques in Process Modeling and Optimization |
| title_full | Neural Networks Applied in Chemistry. II. Neuro-Evolutionary Techniques in Process Modeling and Optimization |
| title_fullStr | Neural Networks Applied in Chemistry. II. Neuro-Evolutionary Techniques in Process Modeling and Optimization |
| title_full_unstemmed | Neural Networks Applied in Chemistry. II. Neuro-Evolutionary Techniques in Process Modeling and Optimization |
| title_short | Neural Networks Applied in Chemistry. II. Neuro-Evolutionary Techniques in Process Modeling and Optimization |
| title_sort | neural networks applied in chemistry ii neuro evolutionary techniques in process modeling and optimization |
| work_keys_str_mv | AT cartwrighth neuralnetworksappliedinchemistryiineuroevolutionarytechniquesinprocessmodelingandoptimization AT curteanus neuralnetworksappliedinchemistryiineuroevolutionarytechniquesinprocessmodelingandoptimization |