{"doi":"10.1109/iconip.1999.844677","title":"Characteristics of associative chaotic neural networks with weighted pattern storage-a pattern is stored stronger than others","abstract":null,"journal":"ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378)","year":null,"id":664805,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"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":1735929,"name":"K. Aihara","orcid":null,"position":1,"is_corresponding":false},{"id":1735928,"name":"M. Adachi","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Characteristics of associative chaotic neural networks with weighted pattern storage-a pattern is stored stronger than others","abstract":"Associative chaotic neural networks with weighted pattern storage are studied. Values of the synaptic weights of conventional associative neural networks are determined by an auto-associative matrix. On the other hand, in this paper, we use a weighted auto-associative matrix in order to store a pattern that is stronger than the other stored patterns. Retrieval characteristics and dynamical properties of associative chaotic neural networks with this weighted auto-associative matrix are numerically analysed. As a result, the network retrieves the strongly stored pattern more frequently than other stored patterns, even in the case where the dynamics of the network is chaotic.","is_dataset_classified":null,"base_score":2.302585092994046,"endowment":2.302585092994046,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19965766","pmcid":null,"openalex_id":"https://openalex.org/W1902246517","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.13165864,"influential_citations":0,"citation_trend":[{"year":2013,"count":2},{"year":2016,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://doi.org/10.1109/iconip.1999.844677","host_type":""}],"fields_of_study":["Neural Networks and Applications","Neural dynamics and brain function","Chaos control and synchronization"],"mesh_terms":[],"keywords":["Associative property","Chaotic","Bidirectional associative memory","Content-addressable memory","Artificial neural network","Content-addressable storage","Computer science","Matrix (chemical analysis)","Pattern recognition (psychology)","Artificial intelligence","Algorithm","Mathematics","Pure mathematics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-13T04:07:53.608129Z","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":[]}