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Uncertainty Treatment Using Paraconsistent Logic Introducing Paraconsistent Artificial Neural Networks 1st Edition by Da Silva Filho, Lambert Torres, Abe 1607505576 978-1607505570

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Uncertainty Treatment Using Paraconsistent Logic Introducing Paraconsistent Artificial Neural Networks 1st Edition J. I. Da Silva Filho Digital Instant Download

Author(s): J. I. da Silva Filho; G. Lambert-Torres; J. M. Abe
ISBN(s): 9781607505587, 1607505584
Edition: 1
File Details: PDF, 6.83 MB
Year: 2010
Language: english
SKU: EB-51642112 Category: Tags: , ,

Uncertainty Treatment Using Paraconsistent Logic Introducing Paraconsistent Artificial Neural Networks 1st Edition by Da Silva Filho, Lambert Torres, Abe – Ebook PDF Instant Download/Delivery: 1607505576, 978-1607505570
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ISBN 10: 1607505576
ISBN 13: 978-1607505570
Author:  J.I. Da Silva Filho, G. Lambert-Torres, J.M. Abe

In the past, control systems for automation and robotics and the expert systems employed in artificial intelligence were generally based on classical, or Boolean, logic. However, this proved to be inadequate by virtue of its binary nature, for portraying the uncertainties and inconsistencies of the real world, and so from the late 1990s, research has been ongoing into the application of paraconsistent, or non-classical logics in these fields.

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.

Notions of Paraconsistent Annotated Logic (PAL) summarizes the basic theory and fundamentals of the subject.

The second part, Paraconsistent Analysis Networks (PANets), describes the utilization of paraconsistent logic in constructing networks which can deal with representative data from uncertain information.

The final section, Paraconsistent Artificial Neural Networks (PANNets), is composed of six chapters which chart the applications of PAL, from a comparison between Paraconsistent Analysis Nodes (PANs) and the action of the human brain through to complex PANNet architecture capable of processing signals inspired by human brain function.

This invaluable state-of-the-art overview will be of interest to all those involved with the development of robotics or artificial intelligence and will serve as reference for future application of paraconsistent logics in all computer and electronic systems.

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Uncertainty Treatment Using Paraconsistent Logic Introducing Paraconsistent Artificial Neural Networks 1st Table of contents:

Chapter 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

Chapter 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..

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