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- From: jberger@asterix.drev.dnd.ca (Jean Berger)
- Newsgroups: comp.ai.neural-nets
- Subject: Dynamic Optimization using ANNs - Refs wanted
- Summary: Request for dynamic optimization models and techniques using ANNs
- Message-ID: <1993Jan12.153327.671@asterix.drev.dnd.ca>
- Date: 12 Jan 93 15:33:27 GMT
- Organization: Defence Research Establishment, Valcartier
- Lines: 30
-
- Look for references and recent experiment using ANNs to solve discrete dynamic
- optimization problems. Traditionally, Hopfield NNs-like systems were used
- to solve classical (static) discrete optimization problem.
- Objective Fctn = f({X(i)}); X(i) = 0 or 1
-
- As an extension to the approach, an evolutionnary framework needs to be
- introduced to incorporate time dependency in decision variables X(i):
- Objective Fctn = f({X(i,time)}); X(i,time) = 0 or 1; time in [0,T]
-
- The nature of this problem is much more complex by the time variable to be
- included in the NN model.
- Should time be discretization-based, state-based (for asynchronous system
- elements), etc. in the ANN model ? Any references ?
-
- If there is sufficient interest on the matter a summary will be posted later.
-
- Thanks in advance,
-
- ==========================================================================
- jean berger Eng. Defence Research Establishment
- Command and Control Division
- Information & Decision Systems Section
- C.P. 8800 Courcelette, Quebec
- Canada, G0A - 1R0
- PHONE: (418) 844-4645
- FAX: (418) 844-4538
-
- E-MAIL: ARPA: jeanb@quebec.drev.dnd.ca
- =========================================================================
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