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| Title: | Optimal management of beaver population using a reduced-order distributed parameter model and single network adaptive critics | |
| Author (s): | Padhi, R. Balakrishnan, S. N. | |
| Department/Lab Affiliations: | Mechanical & Aerospace Engineering | |
| Keywords: | Galerkin method Galerkin projection beaver population control biocontrol distributed parameter control distributed parameter systems neural net architecture neural networks architecture neurocontrollers optimal control optimal management proper orthogonal decomposition reduced order systems reduced-order distributed parameter model single network adaptive critic (SNAC) single network adaptive critics wildlife management zoology | |
| Issue Date: | 2006 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Padhi, R.; Balakrishnan, S. N. "Optimal management of beaver population using a reduced-order distributed parameter model and single network adaptive critics" IEEE Transactions on Control Systems Technology, Vol.14, Iss.4, July 2006 Pages: 628- 640 | |
| Abstract: | Beavers are often found to be in conflict with human interests by creating nuisances like building dams on flowing water (leading to flooding), blocking irrigation canals, cutting down timbers, etc. At the same time they contribute to raising water tables, increased vegetation, etc. Consequently, maintaining an optimal beaver population is beneficial. Because of their diffusion externality (due to migratory nature), strategies based on lumped parameter models are often ineffective. Using a distributed parameter model for beaver population that accounts for their spatial and temporal behavior, an optimal control (trapping) strategy is presented in this paper that leads to a desired distribution of the animal density in a region in the long run. The optimal control solution presented, imbeds the solution for a large number of initial conditions (i.e., it has a feedback form), which is otherwise nontrivial to obtain. The solution obtained can be used in real-time by a nonexpert in control theory since it involves only using the neural networks trained offline. Proper orthogonal decomposition-based basis function design followed by their use in a Galerkin projection has been incorporated in the solution process as a model reduction technique. Optimal solutions are obtained through a "single network adaptive critic" (SNAC) neural-network architecture. | |
| Type: | Article - Journal text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Optimal management of beaver population using a reduced-order distributed parameter model and single network adaptive critics | |
| contributor.author | Padhi, R. | |
| contributor.author | Balakrishnan, S. N. | |
| contributor.deptlab | Mechanical & Aerospace Engineering | |
| subject | Galerkin method | |
| subject | Galerkin projection | |
| subject | beaver population control | |
| subject | biocontrol | |
| subject | distributed parameter control | |
| subject | distributed parameter systems | |
| subject | neural net architecture | |
| subject | neural networks architecture | |
| subject | neurocontrollers | |
| subject | optimal control | |
| subject | optimal management | |
| subject | proper orthogonal decomposition | |
| subject | reduced order systems | |
| subject | reduced-order distributed parameter model | |
| subject | single network adaptive critic (SNAC) | |
| subject | single network adaptive critics | |
| subject | wildlife management | |
| subject | zoology | |
| date.issued | 2006 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Padhi, R.; Balakrishnan, S. N. "Optimal management of beaver population using a reduced-order distributed parameter model and single network adaptive critics" IEEE Transactions on Control Systems Technology, Vol.14, Iss.4, July 2006 Pages: 628- 640 | |
| identifier.issn | 1063-6536 | |
| identifier.pub.URI | ||
| description.abstract | Beavers are often found to be in conflict with human interests by creating nuisances like building dams on flowing water (leading to flooding), blocking irrigation canals, cutting down timbers, etc. At the same time they contribute to raising water tables, increased vegetation, etc. Consequently, maintaining an optimal beaver population is beneficial. Because of their diffusion externality (due to migratory nature), strategies based on lumped parameter models are often ineffective. Using a distributed parameter model for beaver population that accounts for their spatial and temporal behavior, an optimal control (trapping) strategy is presented in this paper that leads to a desired distribution of the animal density in a region in the long run. The optimal control solution presented, imbeds the solution for a large number of initial conditions (i.e., it has a feedback form), which is otherwise nontrivial to obtain. The solution obtained can be used in real-time by a nonexpert in control theory since it involves only using the neural networks trained offline. Proper orthogonal decomposition-based basis function design followed by their use in a Galerkin projection has been incorporated in the solution process as a model reduction technique. Optimal solutions are obtained through a "single network adaptive critic" (SNAC) neural-network architecture. | |
| type | Article - Journal | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:27:02Z | |
| date.available | 2007-04-05T14:27:01Z | |
| identifier.persist.URI | ||
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