{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:INHPF5EOLFVDHN7M4XO7IGA5Z2","short_pith_number":"pith:INHPF5EO","schema_version":"1.0","canonical_sha256":"434ef2f48e596a33b7ece5ddf4181dce8006415d02e1e1f3daebd256ddbb7ff5","source":{"kind":"arxiv","id":"1509.06057","version":1},"attestation_state":"computed","paper":{"title":"Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"physics.comp-ph","authors_text":"Casilla 554, Chile), C. H. L\\'opez-Caraballo, I. Salfate, J. A. Lazz\\'us, La Serena, L. Palma-Chilla (Departamento de F\\'isica y Astronom\\'ia, M. Rivera, P. Rojas, Universidad de La Serena","submitted_at":"2015-09-20T21:09:35Z","abstract_excerpt":"In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the short-term $x(t+6)$. The performance prediction was evaluated and compared with another studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called {\\it stochastic} hybrid "},"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":"1509.06057","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2015-09-20T21:09:35Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"a00fdefdcdf1549440f0333400bcd49fc188727e484a4b6ade14913c39acfaf2","abstract_canon_sha256":"3a572c613a7b3e502012dccb2e7ab572cd9b7f89055f44b03c6ccd1a25567996"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:32:35.977592Z","signature_b64":"r098MoJrPns6kZPKlDkKR5nwyNOMOVivyM77vLPcmN2tigGRws/vVdGmALCzROanE8O4V5VopKXorn6mAYzZAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"434ef2f48e596a33b7ece5ddf4181dce8006415d02e1e1f3daebd256ddbb7ff5","last_reissued_at":"2026-05-18T01:32:35.977146Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:32:35.977146Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"physics.comp-ph","authors_text":"Casilla 554, Chile), C. H. L\\'opez-Caraballo, I. Salfate, J. A. Lazz\\'us, La Serena, L. Palma-Chilla (Departamento de F\\'isica y Astronom\\'ia, M. Rivera, P. Rojas, Universidad de La Serena","submitted_at":"2015-09-20T21:09:35Z","abstract_excerpt":"In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the short-term $x(t+6)$. The performance prediction was evaluated and compared with another studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called {\\it stochastic} hybrid "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.06057","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":"1509.06057","created_at":"2026-05-18T01:32:35.977201+00:00"},{"alias_kind":"arxiv_version","alias_value":"1509.06057v1","created_at":"2026-05-18T01:32:35.977201+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.06057","created_at":"2026-05-18T01:32:35.977201+00:00"},{"alias_kind":"pith_short_12","alias_value":"INHPF5EOLFVD","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_16","alias_value":"INHPF5EOLFVDHN7M","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_8","alias_value":"INHPF5EO","created_at":"2026-05-18T12:29:25.134429+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/INHPF5EOLFVDHN7M4XO7IGA5Z2","json":"https://pith.science/pith/INHPF5EOLFVDHN7M4XO7IGA5Z2.json","graph_json":"https://pith.science/api/pith-number/INHPF5EOLFVDHN7M4XO7IGA5Z2/graph.json","events_json":"https://pith.science/api/pith-number/INHPF5EOLFVDHN7M4XO7IGA5Z2/events.json","paper":"https://pith.science/paper/INHPF5EO"},"agent_actions":{"view_html":"https://pith.science/pith/INHPF5EOLFVDHN7M4XO7IGA5Z2","download_json":"https://pith.science/pith/INHPF5EOLFVDHN7M4XO7IGA5Z2.json","view_paper":"https://pith.science/paper/INHPF5EO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1509.06057&json=true","fetch_graph":"https://pith.science/api/pith-number/INHPF5EOLFVDHN7M4XO7IGA5Z2/graph.json","fetch_events":"https://pith.science/api/pith-number/INHPF5EOLFVDHN7M4XO7IGA5Z2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/INHPF5EOLFVDHN7M4XO7IGA5Z2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/INHPF5EOLFVDHN7M4XO7IGA5Z2/action/storage_attestation","attest_author":"https://pith.science/pith/INHPF5EOLFVDHN7M4XO7IGA5Z2/action/author_attestation","sign_citation":"https://pith.science/pith/INHPF5EOLFVDHN7M4XO7IGA5Z2/action/citation_signature","submit_replication":"https://pith.science/pith/INHPF5EOLFVDHN7M4XO7IGA5Z2/action/replication_record"}},"created_at":"2026-05-18T01:32:35.977201+00:00","updated_at":"2026-05-18T01:32:35.977201+00:00"}