--- abstract: |- Many real-world processes tend to be chaotic and are not amenable to satisfactory analytical models. It has been shown here that for such chaotic processes represented through short chaotic noisy observed data, a multi-input and multi-output recurrent neural network can be built which is capable of capturing the process trends and predicting the behaviour for any given starting condition. It is further shown that this capability can be achieved by the recurrent neural network model when it is trained to very low value of mean squared error. Such a model can then be used for constructing the Bifurcation Diagram of the process leading to determination of desirable operating conditions. Further, this multi-input and multi-output model makes the process accessible for control using open-loop / closed-loop approaches or bifurcation control etc. altloc: - http://www.sciencedirect.com/science?_ob=ArticleURL&_aset=V-WA-A-W-W-MsSAYWW-UUW-U-AACUWADWYV-AAVDDEYUYV-YZYDVBEED-W-U&_rdoc=2&_fmt=summary&_udi=B6V4N-4GKWHWK-1&_coverDate=01%2F31%2F2006&_cdi=5763&_orig=search&_st=13&_sort=d&view=c&_acct=C000050221&_versi chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: [] creators_name: - family: Jallu given: Krishnaiah honourific: '' lineage: '' - family: Kumar given: C.S. honourific: '' lineage: '' - family: Faruqi given: M.A. honourific: '' lineage: '' date: 2006-01 date_type: published datestamp: 2006-05-25 department: ~ dir: disk0/00/00/48/81 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 4881 fileinfo: /style/images/fileicons/application_pdf.png;/4881/1/jpc%2Dmaf2.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: |- Bifurcation Diagram, Recurrent Neural Networks, Multivariate Chaotic Time-series, Chaotic Process lastmod: 2011-03-11 08:56:25 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: 1 pagerange: 67-79 pubdom: FALSE publication: Journal Process Control publisher: Elsevier refereed: TRUE referencetext: ~ relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 17:03:15 subjects: - comp-sci-mach-dynam-sys - comp-sci-mach-learn - comp-sci-neural-nets succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: |- Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Networks models Part 2 - Industrial Study type: journalp userid: 6346 volume: 16