A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener–Hammerstein Models by Using WH-EA and Multistep Signals
Wiener, Hammerstein, and Wiener–Hammerstein structures are useful for modelling dynamic systems that exhibit a static type nonlinearity. Many methods to identify these systems can be found in the literature; however, choosing a method requires prior knowledge about the location of the static nonline...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/7132349 |
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| author | J. Zambrano J. Sanchis J. M. Herrero M. Martínez |
| author_facet | J. Zambrano J. Sanchis J. M. Herrero M. Martínez |
| author_sort | J. Zambrano |
| collection | DOAJ |
| description | Wiener, Hammerstein, and Wiener–Hammerstein structures are useful for modelling dynamic systems that exhibit a static type nonlinearity. Many methods to identify these systems can be found in the literature; however, choosing a method requires prior knowledge about the location of the static nonlinearity. In addition, existing methods are rigid and exclusive for a single structure. This paper presents a unified approach for the identification of Wiener, Hammerstein, and Wiener–Hammerstein models. This approach is based on the use of multistep excitation signals and WH-EA (an evolutionary algorithm for Wiener–Hammerstein system identification). The use of multistep signals will take advantage of certain properties of the algorithm, allowing it to be used as it is to identify the three types of structures without the need for the user to know a priori the process structure. In addition, since not all processes can be excited with Gaussian signals, the best linear approximation (BLA) will not be required. Performance of the proposed method is analysed using three numerical simulation examples and a real thermal process. Results show that the proposed approach is useful for identifying Wiener, Hammerstein, and Wiener–Hammerstein models, without requiring prior information on the type of structure to be identified. |
| format | Article |
| id | doaj-art-e3d830a09dc94e418930ec2285f0160a |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e3d830a09dc94e418930ec2285f0160a2025-08-20T02:21:24ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/71323497132349A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener–Hammerstein Models by Using WH-EA and Multistep SignalsJ. Zambrano0J. Sanchis1J. M. Herrero2M. Martínez3Universidad Politécnica Salesiana, Cuenca, EcuadorInstituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valéncia, SpainInstituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valéncia, SpainInstituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valéncia, SpainWiener, Hammerstein, and Wiener–Hammerstein structures are useful for modelling dynamic systems that exhibit a static type nonlinearity. Many methods to identify these systems can be found in the literature; however, choosing a method requires prior knowledge about the location of the static nonlinearity. In addition, existing methods are rigid and exclusive for a single structure. This paper presents a unified approach for the identification of Wiener, Hammerstein, and Wiener–Hammerstein models. This approach is based on the use of multistep excitation signals and WH-EA (an evolutionary algorithm for Wiener–Hammerstein system identification). The use of multistep signals will take advantage of certain properties of the algorithm, allowing it to be used as it is to identify the three types of structures without the need for the user to know a priori the process structure. In addition, since not all processes can be excited with Gaussian signals, the best linear approximation (BLA) will not be required. Performance of the proposed method is analysed using three numerical simulation examples and a real thermal process. Results show that the proposed approach is useful for identifying Wiener, Hammerstein, and Wiener–Hammerstein models, without requiring prior information on the type of structure to be identified.http://dx.doi.org/10.1155/2020/7132349 |
| spellingShingle | J. Zambrano J. Sanchis J. M. Herrero M. Martínez A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener–Hammerstein Models by Using WH-EA and Multistep Signals Complexity |
| title | A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener–Hammerstein Models by Using WH-EA and Multistep Signals |
| title_full | A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener–Hammerstein Models by Using WH-EA and Multistep Signals |
| title_fullStr | A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener–Hammerstein Models by Using WH-EA and Multistep Signals |
| title_full_unstemmed | A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener–Hammerstein Models by Using WH-EA and Multistep Signals |
| title_short | A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener–Hammerstein Models by Using WH-EA and Multistep Signals |
| title_sort | unified approach for the identification of wiener hammerstein and wiener hammerstein models by using wh ea and multistep signals |
| url | http://dx.doi.org/10.1155/2020/7132349 |
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