This site has been permanently archived. This is a static copy provided by the University of Southampton.
--- abstract: "Many real-world processes tend to be chaotic and also do not lead to satisfactory analytical modelling. It has been shown here that for such chaotic processes represented through short chaotic noisy time-series, a multi-input and multi-output recurrent neural networks model can be built which is capable of capturing the process trends and predicting the future values from 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. All these studies have been carried out using a low dimensional discrete chaotic system of Hénon Map as a representative of some real-world processes. \n\n" altloc: - http://dx.doi.org/10.1016/j.jprocont.2005.04.002 chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: - j.krishnaiah@gmail.com - '' - '' creators_name: - family: J given: Krishnaiah honourific: '' lineage: '' - family: C given: Kumar honourific: S lineage: '' - family: Faruqi given: Aslam honourific: M lineage: '' date: 2006-01 date_type: published datestamp: 2006-05-25 department: ~ dir: disk0/00/00/48/83 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 4883 fileinfo: /style/images/fileicons/application_pdf.png;/4883/1/jcp_maf1.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: 53-66 pubdom: FALSE publication: Journal of Process Control publisher: ~ refereed: TRUE referencetext: ~ relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 17:03:17 subjects: - comp-sci-mach-dynam-sys - comp-sci-mach-learn - comp-sci-complex-theory - comp-sci-art-intel succeeds: 4842 suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: 'Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1—simulation studies ' type: journalp userid: 6346 volume: 16