{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AXV3TRANDXTOJP2FPFLWO6ALTY","short_pith_number":"pith:AXV3TRAN","schema_version":"1.0","canonical_sha256":"05ebb9c40d1de6e4bf45795767780b9e0573992c6b723f087604c91dd69afc08","source":{"kind":"arxiv","id":"2607.17065","version":1},"attestation_state":"computed","paper":{"title":"A recursive subspace based method for errors-in-variables model identification of time-varying systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Deepanjhan Das, Shankar Narasimhan","submitted_at":"2026-07-19T04:20:04Z","abstract_excerpt":"The Subspace-based Model Identification algorithm using a modified Iterative Principal Component Analysis (SMI-IPCA) is a theoretically rigorous method for identifying a linear state-space model of a multi-input multi-output (MIMO) process, in an errors-in-variables (EIV) setting. The method can simultaneously estimate unknown heteroskedastic noise variances corrupting the input and output measurements, along with the state space model. This work proposes a recursive formulation of SMI-IPCA (RSMI-IPCA) enabling online identification and adaptive model updates as and when new data arrive. By ma"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.17065","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-07-19T04:20:04Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"e4af8332c7164d9e6cd32ccb145d727fec2ef33ae9cba85650f9dcf7e20b6001","abstract_canon_sha256":"4d267340de8878e627f02dded778d10c11b6130494c8f8991b36ea93cbf5ccd1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T01:21:13.063667Z","signature_b64":"YhUjoeBiQPYBNIoyB0VvZc28EDb1nwj2oHOEUClL3VYD7hh5O8II+g6exJQW2hsDajz4BIe+fnRvL2/qAZCDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05ebb9c40d1de6e4bf45795767780b9e0573992c6b723f087604c91dd69afc08","last_reissued_at":"2026-07-21T01:21:13.062867Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T01:21:13.062867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A recursive subspace based method for errors-in-variables model identification of time-varying systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Deepanjhan Das, Shankar Narasimhan","submitted_at":"2026-07-19T04:20:04Z","abstract_excerpt":"The Subspace-based Model Identification algorithm using a modified Iterative Principal Component Analysis (SMI-IPCA) is a theoretically rigorous method for identifying a linear state-space model of a multi-input multi-output (MIMO) process, in an errors-in-variables (EIV) setting. The method can simultaneously estimate unknown heteroskedastic noise variances corrupting the input and output measurements, along with the state space model. This work proposes a recursive formulation of SMI-IPCA (RSMI-IPCA) enabling online identification and adaptive model updates as and when new data arrive. By ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17065","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.17065/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.17065","created_at":"2026-07-21T01:21:13.063282+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.17065v1","created_at":"2026-07-21T01:21:13.063282+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17065","created_at":"2026-07-21T01:21:13.063282+00:00"},{"alias_kind":"pith_short_12","alias_value":"AXV3TRANDXTO","created_at":"2026-07-21T01:21:13.063282+00:00"},{"alias_kind":"pith_short_16","alias_value":"AXV3TRANDXTOJP2F","created_at":"2026-07-21T01:21:13.063282+00:00"},{"alias_kind":"pith_short_8","alias_value":"AXV3TRAN","created_at":"2026-07-21T01:21:13.063282+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AXV3TRANDXTOJP2FPFLWO6ALTY","json":"https://pith.science/pith/AXV3TRANDXTOJP2FPFLWO6ALTY.json","graph_json":"https://pith.science/api/pith-number/AXV3TRANDXTOJP2FPFLWO6ALTY/graph.json","events_json":"https://pith.science/api/pith-number/AXV3TRANDXTOJP2FPFLWO6ALTY/events.json","paper":"https://pith.science/paper/AXV3TRAN"},"agent_actions":{"view_html":"https://pith.science/pith/AXV3TRANDXTOJP2FPFLWO6ALTY","download_json":"https://pith.science/pith/AXV3TRANDXTOJP2FPFLWO6ALTY.json","view_paper":"https://pith.science/paper/AXV3TRAN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.17065&json=true","fetch_graph":"https://pith.science/api/pith-number/AXV3TRANDXTOJP2FPFLWO6ALTY/graph.json","fetch_events":"https://pith.science/api/pith-number/AXV3TRANDXTOJP2FPFLWO6ALTY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AXV3TRANDXTOJP2FPFLWO6ALTY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AXV3TRANDXTOJP2FPFLWO6ALTY/action/storage_attestation","attest_author":"https://pith.science/pith/AXV3TRANDXTOJP2FPFLWO6ALTY/action/author_attestation","sign_citation":"https://pith.science/pith/AXV3TRANDXTOJP2FPFLWO6ALTY/action/citation_signature","submit_replication":"https://pith.science/pith/AXV3TRANDXTOJP2FPFLWO6ALTY/action/replication_record"}},"created_at":"2026-07-21T01:21:13.063282+00:00","updated_at":"2026-07-21T01:21:13.063282+00:00"}