1 / 10

Active Sonar Target Identification Using Evolutionary Neural Logic Networks

Active Sonar Target Identification Using Evolutionary Neural Logic Networks. Athanasios Tsakonas Dept. of Financial and Management Engineering, University of the Aegean , Greece Georgios Dounias Dept. of Financial and Management Engineering, University of the Aegean , Greece

riona
Download Presentation

Active Sonar Target Identification Using Evolutionary Neural Logic Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Active Sonar Target Identification Using Evolutionary Neural Logic Networks Athanasios Tsakonas Dept. of Financial and Management Engineering, University of the Aegean, Greece Georgios DouniasDept. of Financial and Management Engineering, University of the Aegean, Greece Nikitas Nikitakos Dept. of Shipping, Trade and Finance, University of the Aegean, Greece Presenting Author:Emmanouil Vasilakis, University of the Aegean, Greece

  2. Contents • Neural-Symbolic Systems and Integration • Neural Logic Networks • Expressing NLNs into PROLOG rules • Constructing NLNs from data • Past approaches • The Evolutionary NLNs • Applications • Summary NeSy-2006, Riva Del Garda

  3. Neural – Symbolic Integration Unification systems Hybrid Systems Neuronal Modeling/Neuroscience Connectionist Logic Systems Hybrid Systems by Translation Hybrid Systems by Function Neural-Symbolic Systems and Integration Source: D’Avila Garcez (2002) NeSy-2006, Riva Del Garda

  4. Neural Logic Networks • Finite directed graph • Consisted by a set of input nodes and an output node • The possible value for a node can be one of three ordered pair activation values (1,0) for true, (0,1) for false and (0,0) for don't know NeSy-2006, Riva Del Garda

  5. Expressing NLNs into PROLOG rules • We may create rules into the programming language PROLOG directly by every neural logic network. NeSy-2006, Riva Del Garda

  6. Constructing NLNs from data : Past approaches • Tan et al. 1996 • Teh 1995 NeSy-2006, Riva Del Garda

  7. Constructing NLNs from data : Past approaches • Chia and Tan 2001 NeSy-2006, Riva Del Garda

  8. Constructing NLNs from data : the Evolutionary NLNs NeSy-2006, Riva Del Garda

  9. Active Sonar Identification • 86.27 % in test set (CNLN (P1 (P1 (P1 (S1 (S1 (In T10) (Rule 0 0) E) (Rule 0 0) (S2 E (Rule 0 0) E)) (P1 (S1 (In T4) (Rule 0 0) (P2 E (Rule 10 3) (S2 E (Rule 10 3) E))) (In T11))) (P1 (P1 (In T3) (S1 (In T48) (Rule 12 8) E)) (P1 (P1 (P1 (S1 (S1 (In T10) (Rule 0 0) E) (Rule 0 0) (S2 E (Rule 0 0) E)) (P1 (S1 (S1 (In T4) (Link 50 0 (Rule 0 0)) E) (Rule 10 3) E) (P1 (S1 (In T4) (Rule 12 8) (P2 E (Rule 10 3) (S2 E (Rule 0 0) E))) (In T11)))) (P1 (P1 (In T58) (S1 (In T24) (Rule 12 8) (P2 E (Link 133 0 (Rule 0 0)) E))) (P1 (P1 (S1 (In T52) (Rule 0 0) E) (P1 (S1 (In T4) (Link 133 0 (Rule 10 3)) (P2 E (Rule 0 0) (S2 E (Rule 0 0) E))) (P1 (S1 (In T28) (Rule 0 0) (P2 E (Rule 10 3) (S2 E (Rule 0 0) E))) (In T11)))) (P1 (P1 (In T58) (S1 (In T24) (Rule 12 8) (P2 E (Link 133 0 (Rule 0 0)) E))) (P1 (P1 (S1 (In T31) (Rule 10 3) E) (In T49)) (In T42)))))) (In T4)))) (In T50)) (Rule 2 8)) NeSy-2006, Riva Del Garda

  10. Summary • Neural-Symbolic Integration • Neural Logic Network • We proposed an evolutionary technique that uses: • Cellular encoding • Genetic programming • Grammar-based search guidance • Results – Application in Active Sonar Identification NeSy-2006, Riva Del Garda

More Related