{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:RTRQZ6UG25TRXWXZLCIFDB5HL7","short_pith_number":"pith:RTRQZ6UG","schema_version":"1.0","canonical_sha256":"8ce30cfa86d7671bdaf958905187a75fe933ffba4db29eeded78ff86352677d8","source":{"kind":"arxiv","id":"1705.00945","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Chih-Min Lin, Hao-Chun Chu, Junghsi Lee, Shih-Hau Fang, Shih-Wei Lan, Yu Tsao","submitted_at":"2017-05-02T12:54:48Z","abstract_excerpt":"This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking sin-gle-layer CMAC models into multiple layers to form a DCMAC model and derive a modified backpropagation training algorithm to learn the DCMAC parameters. Com-pared with conventional CMAC, the DCMAC can characterize nonlinear transformations more effectively because of its deep structure. Experimental results confirm that the pro-posed DCMAC model outperforms the CMAC in terms of residual noise in an ANC task, showing that DCMAC p"},"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":"1705.00945","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2017-05-02T12:54:48Z","cross_cats_sorted":[],"title_canon_sha256":"b947d97a031091120fad13647515e8d44e3e1c712f77f0b3cee20c730ddbfab0","abstract_canon_sha256":"11bc41a37c3f4964cfa3fd35a19e5b43430dc86c5400e065c15f4058b725ae6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:09.294791Z","signature_b64":"PPOf4YKSyP0uThRIWN+GwE5dKYYjFwabSEY1DiIY4n4K1h1vw/MpyIRw6TNxuXepO2o22H1a9d/rwHkv/xLHBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ce30cfa86d7671bdaf958905187a75fe933ffba4db29eeded78ff86352677d8","last_reissued_at":"2026-05-18T00:45:09.294188Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:09.294188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Chih-Min Lin, Hao-Chun Chu, Junghsi Lee, Shih-Hau Fang, Shih-Wei Lan, Yu Tsao","submitted_at":"2017-05-02T12:54:48Z","abstract_excerpt":"This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking sin-gle-layer CMAC models into multiple layers to form a DCMAC model and derive a modified backpropagation training algorithm to learn the DCMAC parameters. Com-pared with conventional CMAC, the DCMAC can characterize nonlinear transformations more effectively because of its deep structure. Experimental results confirm that the pro-posed DCMAC model outperforms the CMAC in terms of residual noise in an ANC task, showing that DCMAC p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.00945","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":""},"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":"1705.00945","created_at":"2026-05-18T00:45:09.294281+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.00945v1","created_at":"2026-05-18T00:45:09.294281+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.00945","created_at":"2026-05-18T00:45:09.294281+00:00"},{"alias_kind":"pith_short_12","alias_value":"RTRQZ6UG25TR","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"RTRQZ6UG25TRXWXZ","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"RTRQZ6UG","created_at":"2026-05-18T12:31:39.905425+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/RTRQZ6UG25TRXWXZLCIFDB5HL7","json":"https://pith.science/pith/RTRQZ6UG25TRXWXZLCIFDB5HL7.json","graph_json":"https://pith.science/api/pith-number/RTRQZ6UG25TRXWXZLCIFDB5HL7/graph.json","events_json":"https://pith.science/api/pith-number/RTRQZ6UG25TRXWXZLCIFDB5HL7/events.json","paper":"https://pith.science/paper/RTRQZ6UG"},"agent_actions":{"view_html":"https://pith.science/pith/RTRQZ6UG25TRXWXZLCIFDB5HL7","download_json":"https://pith.science/pith/RTRQZ6UG25TRXWXZLCIFDB5HL7.json","view_paper":"https://pith.science/paper/RTRQZ6UG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.00945&json=true","fetch_graph":"https://pith.science/api/pith-number/RTRQZ6UG25TRXWXZLCIFDB5HL7/graph.json","fetch_events":"https://pith.science/api/pith-number/RTRQZ6UG25TRXWXZLCIFDB5HL7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RTRQZ6UG25TRXWXZLCIFDB5HL7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RTRQZ6UG25TRXWXZLCIFDB5HL7/action/storage_attestation","attest_author":"https://pith.science/pith/RTRQZ6UG25TRXWXZLCIFDB5HL7/action/author_attestation","sign_citation":"https://pith.science/pith/RTRQZ6UG25TRXWXZLCIFDB5HL7/action/citation_signature","submit_replication":"https://pith.science/pith/RTRQZ6UG25TRXWXZLCIFDB5HL7/action/replication_record"}},"created_at":"2026-05-18T00:45:09.294281+00:00","updated_at":"2026-05-18T00:45:09.294281+00:00"}