Learning soft computer control strategies in a modular neural network architecture
dc.contributor.author | sharma, sanjay | |
dc.contributor.author | Irwin, GW | |
dc.contributor.author | Tokhi, MO | |
dc.contributor.author | McLoone, SF | |
dc.date.accessioned | 2017-02-15T16:37:01Z | |
dc.date.available | 2017-02-15T16:37:01Z | |
dc.date.issued | 2003-08-06 | |
dc.identifier.issn | 0952-1976 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/8500 | |
dc.description.abstract |
Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. | |
dc.format.extent | 395-405 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Elsevier BV | |
dc.title | Learning soft computer control strategies in a modular neural network architecture | |
dc.type | journal-article | |
plymouth.issue | 5-6 | |
plymouth.volume | 16 (5-6) | |
plymouth.publication-status | Published | |
plymouth.journal | Engineering Applications of Artificial Intelligence | |
dc.identifier.doi | 10.1016/S0952-1976(03)00070-8 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering | |
plymouth.organisational-group | /Plymouth/Research Groups | |
plymouth.organisational-group | /Plymouth/Research Groups/Marine Institute | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dcterms.dateAccepted | 2003-06-11 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1016/S0952-1976(03)00070-8 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review |