{"doi":"10.1504/ijmic.2012.048641","title":"Least squares support kernel machines (LS-SKM) for identification","abstract":null,"journal":"International Journal of Modelling, Identification and Control","year":2012,"id":47024,"datarank":0.5964751317783488,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.26689144517791585,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.26689144517791585,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":6,"citers_with_citation_signal":5,"citers_with_endowment":5,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":217232,"name":"Salah Zidi","orcid":null,"position":1,"is_corresponding":false},{"id":217233,"name":"Kaouther Laabidi","orcid":null,"position":2,"is_corresponding":false},{"id":217234,"name":"M. Ksouri Lahmari","orcid":null,"position":3,"is_corresponding":false},{"id":217231,"name":"Mounira Tarhouni","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Least squares support kernel machines (LS-SKM) for identification","abstract":"This paper presents a novel approach for non-linear systems identification called ‘least squares support kernel machines (LS-SKM)’. Instead of using a least squares support vector machines (LS-SVM) with a single kernel function, the proposed approach combines several kernels in order to take advantage of their performances and also reflects the fact that practical learning problems often involve multiple, heterogeneous data sources. The idea is to divide the regressor vector in several regressor vectors, and, for each vector a kernel function is used. The choice of kernel function and the corresponding parameters is an important task which is related to the non-linear system degrees. A constrained particle swarm optimisation (CPSO) is used to give solution for the determination of optimised kernel parameters. Two examples are presented for qualitative comparison with the classical LS-SVM. The results reveal the accuracy and the robustness of the obtained model based on our proposed hybrid method.","is_dataset_classified":null,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W1968282683","authors":[],"funders":[],"total_grants":0,"fwci":3.3686,"citation_percentile":0.91899021,"influential_citations":0,"citation_trend":[{"year":2013,"count":2},{"year":2014,"count":3},{"year":2015,"count":1},{"year":2017,"count":1},{"year":2025,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://doi.org/10.1504/ijmic.2012.048641","host_type":"journal"}],"fields_of_study":["Advanced Algorithms and Applications","Structural Health Monitoring Techniques","Neural Networks and Applications","Mathematics","Computer Science","Engineering"],"mesh_terms":[],"keywords":["Least squares support vector machine","Kernel (algebra)","Support vector machine","Radial basis function kernel","Kernel method","Polynomial kernel","Least-squares function approximation","Computer science","Robustness (evolution)","Variable kernel density estimation","Identification (biology)","Algorithm","Artificial intelligence","Mathematical optimization","Mathematics","Pattern recognition (psychology)","Statistics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-15T05:21:37.904961Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}