Research Catalog

Uncertainty treatment using paraconsistent logic : introducing paraconsistent artificial neural networks

Title
Uncertainty treatment using paraconsistent logic : introducing paraconsistent artificial neural networks / João Inácio da Silva Filho, Germano Lambert-Torres and Jair Minoro Abe.
Author
Silva Filho, João Inácio da.
Publication
Amsterdam ; Washington, DC : IOS Press, ©2010.

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Details

Additional Authors
  • Torres, Germano Lambert.
  • Abe, Jair Minoro.
Description
xiv, 311 p. : ill.; 25 cm.
Summary
This book aggregates much of this research, from 1999 up to the present. Organized to facilitate an understanding of the theory and the development of the applied methods, Uncertainty Treatment Using Praconsistent Logic presents the material in a sequential fashion and is divided into three parts.
Series Statement
Frontiers in artificial intelligence and applications, 0922-6389 ; v. 211. Knowledge-based intelligent engineering systems
Uniform Title
  • Frontiers in artificial intelligence and applications ; v. 211.
  • Frontiers in artificial intelligence and applications. Knowledge-based intelligent engineering systems
Subject
  • Neural networks (Computer science)
  • Uncertainty (Information theory)
  • Logic programming
  • Inconsistency (Logic)
  • Artificial intelligence
  • Neural Networks, Computer
  • Artificial Intelligence
  • artificial intelligence
Bibliography (note)
  • Includes bibliographical references (p. 309-311).
Contents
  • Machine generated contents note: A.1.Objectives -- A.2.Organization of the book -- pt. 1 Paraconsistent Annotated Logic (PAL) -- ch. 1 Basic Notions of Paraconsistent Annotated Logic (PAL) -- Introduction -- 1.1.Logic -- 1.2.The Non-Classical Logic -- 1.3.Paraconsistent Logic -- 1.3.1.Historical Aspects of Paraconsistent Logic -- 1.3.2.Inconsistent Theories and Trivial Theories -- 1.3.3.Conceptual Principles of Paraconsistent Logic -- 1.4.Paraconsistent Annotated Logic -- 1.4.1.Representation of Paraconsistent Annotated Logic (PAL) -- 1.4.2.First Order Paraconsistent Annotated Logic Language -- 1.4.3.A Single Valued Paraconsistent Annotated Logic -- 1.5.Paraconsistent Annotated Logic with Annotation of Two Values (PAL2v) -- 1.5.1.PAL2v Language Primitive Symbols -- 1.5.2.Considerations on Lattice Associated to Paraconsistent Annotated Logic with Annotation of Two Values (PAL2v) -- 1.5.3.The Logic Negation of PAL2v -- 1.6.Final Remarks -- Exercises -- ch. 2 Paraconsistent Annotated Logic Application Methodology -- Introduction -- 2.1.Paraconsistent Logic in Uncertain Knowledge Treatment -- 2.2.Algebraic Interpretations of PAL2v -- 2.2.1.The Unitary Square on the Cartesian Plane (USCP) -- 2.2.2.Algebraic Relations Between the USCP and the PAL2v Lattice -- 2.2.3.Geometric Relations Between the USCP and the PAL2v Lattice -- 2.3.The Para-Analyzer Algorithm -- 2.3.1.Paraconsistent Annotated Logic with Annotation of Two Values "Para-Analyzer" Algorithm -- 2.4.Para-Analyzer Algorithm Application -- 2.5.Final Remarks -- Exercises -- pt. 2 Paraconsistent Analysis Networks (PANet) -- ch. 3 Fundamentals of Paraconsistent Analysis Systems -- Introduction -- 3.1.Uncertainty Treatment Systems for Decision Making -- 3.2.Uncertainty Treatment Systems for Decision Making Using PAL2v -- 3.2.1.Study on the Representation of the PAL2v Lattice for Uncertainty Treatment -- 3.2.2.The Interval of Certainty -- 3.2.3.Representation of the Resultant Degree of Certainty -- 3.2.4.The Estimated Degree of Certainty -- 3.2.5.Input Data Variations in Relation to the Estimated Degree of Certainty -- 3.2.6.The Real Degree of Certainty -- 3.2.7.The Influence of Contradiction on the Real Degree of Certainty -- 3.2.8.Representation of the Real Resultant Interval of Certainty -- 3.2.9.Recovering the Values of Degrees of Certainty and Contradiction -- 3.3.Algorithms for Uncertainty Treatment Through Paraconsistent Analysis -- 3.3.1.PAL2v Paraconsistent Analysis Algorithm with Resultant Degree of Certainty Output -- 3.3.2.PAL2v Paraconsistent Analysis Algorithm to Estimate the Degrees of Certainty and Evidence Input Values -- 3.3.3.PAL2v Paraconsistent System Algorithm with Feedback -- 3.4.Final Remarks -- Exercises -- ch. 4 Paraconsistent Analysis System Configurations -- Introduction -- 4.1.Typical Paraconsistent Analysis Node (PAN) -- 4.1.1.Paraconsistent Analysis Node-PAN Rules -- 4.1.2.Transformation of the Real Degree of Certainty into Resultant Degree of Evidence -- 4.1.3.Resultant Real Degree of Evidence ER -- 4.1.4.The Normalized Degree of Contradiction ctr -- 4.1.5.The Resultant Interval of Evidence E -- 4.2.The Algorithms of the Paraconsistent Analysis Nodes (PANs) -- 4.2.1.PAL2v Paraconsistent Analysis Algorithm with Resultant Real Degree of Evidence Output -- 4.2.2.PAL2v Paraconsistent Analysis Algorithm with Calculus of the Normalized Degree of Contradiction and Interval of Evidence -- 4.3.Final Remarks -- Exercises -- ch. 5 Modeling of Paraconsistent Logical Signals -- Introduction -- 5.1.Contradiction and Paraconsistent Logic -- 5.1.1.PAL2v Annotation Modeling -- 5.1.2.Applications of Models for the Mining of Degrees of Evidence -- 5.2.Treatment of Contradiction in the Modeling of the Evidence Signal -- 5.3.Final Remarks -- Exercises -- ch. 6 Paraconsistent Analysis Network for Uncertainty Treatment -- Introduction -- 6.1.Paraconsistent Analysis Network (PANet) -- 6.1.1.Rules for Paraconsistent Analysis Network -- 6.1.2.Basic Configuration of a Paraconsistent Analysis Network -- 6.1.3.Paraconsistent Analysis Networks Algorithms and Topologies -- 6.1.4.PAL2v Paraconsistent Analysis Algorithm with the Disabling of the PAN Due to Indefinition -- 6.2.Three-Dimensional Paraconsistent Analysis Network -- 6.2.1.Paraconsistent Analyzer Cube -- 6.2.2.Construction of a Paraconsistent Analyzer Cube -- 6.3.Algorithms of the Paraconsistent Analyzer Cube -- 6.3.1.Modeling of the Paraconsistent Analyzer Cube with the Value of the External Interval of Evidence -- 6.3.2.Paraconsistent Analyzer Cube Algorithm Modeled with Interval of Evidence -- 6.3.3.Modeling of a Paraconsistent Analyzer Cube with the Value of the External Degree of Contradiction -- 6.3.4.Paraconsistent Analyzer Cube Algorithm with the External Degree of Contradiction -- 6.4.Paraconsistent Analysis Network Topologies with Analyzer Cubes -- 6.4.1.Paraconsistent Analysis Network with PAN and one Paraconsistent Analyzer Cube -- 6.4.2.Analysis Network with Inconsistency Filter Composed of Paraconsistent Analyzer Cubes -- 6.5.Final Remarks -- Exercises -- pt. 3 Paraconsistent Artificial Neural Networks (PANNets) -- ch. 7 Paraconsistent Artificial Neural Cell -- Introduction -- 7.1.Neural Computation and Paraconsistent Logic -- 7.1.1.A Basic Paraconsistent Artificial Cell (bPAC) -- 7.2.The Standard Paraconsistent Artificial Neural Cell (sPANC) -- 7.2.1.sPANC Fundamental Concepts -- 7.3.Composition of the Standard Paraconsistent Artificial Neural Cell (sPANC) -- 7.3.1.Algorithm of the Standard Paraconsistent Artificial Neural Cell (sPANC) -- 7.4.Final Remarks -- Exercises -- ch. 8 Paraconsistent Artificial Neural Cell Family -- Introduction -- 8.1.Family of Paraconsistent Artificial Neural Cells -- 8.2.The Analytical Paraconsistent Artificial Neural Cell (aPANC) -- 8.2.1.Algorithm of the Analytical Paraconsistent Artificial Neural Cell (aPANC) -- 8.3.The Real Analytical Paraconsistent Artificial Neural Cell (RaPANC) -- 8.3.1.Algorithm of the Real Analytical Paraconsistent Artificial Neural Cell (RaPANC) -- 8.4.The Paraconsistent Artificial Neural Cell of Simple Logical Connection (PANCSiLC) -- 8.4.1.Algorithm of the Paraconsistent Artificial Neural Cell of Simple Logical Connection (PANCSiLC) -- 8.5.The Paraconsistent Artificial Neural Cell of Selective Logic Connection (PANCSeLC) -- 8.5.1.Algorithm of the Paraconsistent Artificial Neural Cell of Selective Logical Connection (PANCSeLC) -- 8.6.Crossing Paraconsistent Artificial Neural Cell (cPANC) -- 8.6.1.Algorithm of the Crossing Paraconsistent Artificial Neural Cell (cPANC) -- 8.7.Paraconsistent Artificial Neural Cell of Complementation (PANCc) -- 8.7.1.Algorithm of the Paraconsistent Artificial Neural Cell of Complementation (PANCc) -- 8.8.Paraconsistent Artificial Neural Cell of Equality Detection (PANCED) -- 8.8.1.Algorithm of the Paraconsistent Artificial Neural Cell of Equality Detection (PANCED) -- 8.9.Paraconsistent Artificial Neural Cell of Decision (PANCD) -- 8.9.1.Algorithm of the Paraconsistent Artificial Neural Cell of Decision (PANCD) -- 8.10.Crossing Paraconsistent Artificial Neural Cell of Decision (cPANCD) -- 8.10.1.Algorithm of the Crossing Paraconsistent Artificial Neural Cell of Decision (cPANCD) -- 8.11.Final Remarks -- Exercises -- ch. 9 Learning Paraconsistent Artificial Neural Cell -- Introduction -- 9.1.Learning Paraconsistent Artificial Neural Cell (lPANC) -- 9.1.1.Learning of a Paraconsistent Artificial Neural Cell -- 9.1.2.Algorithm of the Learning Paraconsistent Artificial Neural Cell (lPANC) (for Truth Pattern) -- 9.1.3.Algorithm of the Learning Paraconsistent Artificial Neural Cell (lPANC) (for Falsehood Pattern) -- 9.1.4.Recognition of the Pattern to be Learned -- 9.1.5.Unlearning of a Paraconsistent Artificial Neural Cell -- 9.2.Studies on the Complete Algorithm of the lPANC with Learning and Unlearning -- 9.2.1.Complete Algorithm of the Learning of the Paraconsistent Artificial Neural Cell (lPANC) -- 9.3.Results Obtained in the Training of a Learning Paraconsistent Artificial Neural Cell (lPANC) -- 9.4.Training of a lPANC with the Maximum Values of the Learning (1FT) and Unlearning (u1FT) Factors -- 9.4.1.Simplified Representation -- 9.4.2.lPANC Tests with Variations in the Values of the Learning 1FT and Unlearning u1FT Factors -- 9.4.3.lPANC Tests with Applications of Several Patterns of Different Values and Maximum Learning Factor -- 9.5.Final Remarks -- Exercises -- ch. 10 Paraconsistent Artificial Neural Units -- Introduction -- 10.1.Para-Perceptron -- The The Paraconsistent Artificial Neuron -- 10.2.The Biological Neuron -- 10.3.The Artificial Neuron -- 10.4.Composition of the Paraconsistent Artificial Neuron Para-Perceptron -- 10.4.1.Learning Algorithm with the Inclusion of the Crossing Cell of Decision -- 10.5.Para-Perceptron Models -- 10.6.Test of a Typical Paraconsistent Artificial Neural Para-Perceptron -- 10.7.Other Types of Paraconsistent Artificial Neural Units (PANUs) -- 10.7.1.The Learning Paraconsistent Artificial Neural Unit with Activation Through Maximization (lPANUAM) -- 10.7.2.Learning Paraconsistent Artificial Neural Unit of Control and Pattern Activation (lPANUCPA) -- 10.7.3.Learning Paraconsistent Artificial Neural Unit with Instantaneous Analysis (lPANU1A) -- 10.7.4.Learning Paraconsistent Artificial Neural Unit Through Pattern Equality (lPANUPE) -- 10.7.5.Learning Paraconsistent Artificial Neural Unit Through Repetition of Pattern Pairs (lPANURPP) -- 10.7.6.The Paraconsistent Artificial Neural Unit with Maximum Function (PANUmaxf) -- 10.7.7.The Paraconsistent Artificial Neural Unit with Minimum Function (PANUmimf) -- 10.7.8.The Paraconsistent Artificial Neural Unit of Selective Competition (PANUSeC) -- 10.7.9.The Paraconsistent Artificial Neural Unit of Pattern Activation (PANUPact) -- 10.8.Final Remarks -- Exercises -- ch. 11 Paraconsistent Artificial Neural Systems -- Introduction
  • Note continued: 11.1.Paraconsistent Artificial Neural System of Conditioned Learning -- PANSCL -- 11.1.1.Conditioned Learning -- 11.2.Basic Configuration of the PANSCL -- 11.2.1.Test with PANSCL -- 11.3.Paraconsistent Artificial Neural System and Contradiction Treatment (PANSCT) -- 11.3.1.Pattern Generator for the PANSCT -- 11.3.2.PANSCT Block Diagram -- 11.3.3.The Basic Configuration of the PANSCT -- 11.3.4.Tests with PANSCT -- 11.4.Final Remarks -- Exercises -- ch. 12 Architecture of the Paraconsistent Artificial Neural Networks -- Introduction -- 12.1.Proposal of the Paraconsistent Artificial Neural Networks Architecture -- 12.1.1.Description of the PANNet Functioning -- 12.2.Learning, Comparison, and Signal Analysis Modules of PANNet -- 12.2.1.Paraconsistent Artificial Neural Unit of Primary Learning and Pattern Consultation -- 12.2.2.Paraconsistent Artificial Neural Unit of Pattern Activation -- 12.2.3.Paraconsistent Artificial Neural Unit of Selective Competition -- 12.2.4.Paraconsistent Artificial Neural System of Knowledge Acquisition (PANSKA) -- 12.3.Logical Reasoning Module for the Control of a PANNet -- 12.3.1.The Paraconsistent Artificial Neural Network of Logical Reasoning, PANNLR -- 12.3.2.Configuration of the Paraconsistent Artificial Neural Network System of Logical Reasoning (PANSLR) -- 12.3.3.Paraconsistent Artificial Neural System of Logical Reasoning of Minimization (PANSLRMin) -- 12.3.4.Paraconsistent Artificial Neural System of Logical Reasoning of Maximization (PANSLRMax) -- 12.3.5.Paraconsistent Artificial Neural System of Exclusive OR Logical Reasoning (PANSExORLR) -- 12.3.6.Paraconsistent Artificial Neural System of Complete Logical Reasoning (PANSCLR) -- 12.4.Final Remarks -- Exercises -- Final Comments -- Introduction -- E.1.Applications -- E.2.Final Remarks.
ISBN
  • 1607505576
  • 9781607505570
  • 9781607505587
  • 1607505584
LCCN
2010926677
OCLC
  • ocn645493391
  • 645493391
  • SCSB-9173407
Owning Institutions
Princeton University Library