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Active Sonar Target Identification Using Evolutionary Neural Logic Networks

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Title: AN ARCHITECTURE-ALTERING AND TRAINING METHODOLOGY FOR NEURAL LOGIC NETWORKS Application in the banking sector Author: A.Tsakonas Last modified by – PowerPoint PPT presentation

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Title: Active Sonar Target Identification Using Evolutionary Neural Logic Networks


1
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
  • 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

3
Neural-Symbolic Systems and Integration
Source DAvila Garcez (2002)
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

5
Expressing NLNs into PROLOG rules
  • We may create rules into the programming language
    PROLOG directly by every neural logic network.

6
Constructing NLNs from data Past approaches
  • Tan et al. 1996
  • Teh 1995

7
Constructing NLNs from data Past approaches
  • Chia and Tan 2001

8
Constructing NLNs from data the Evolutionary
NLNs
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))
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
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