scholarly journals On the Knowledge-Based Dynamic Fuzzy Sets

2019 ◽  
pp. 293-302
Author(s):  
Rolly Intan ◽  
Siana Halim ◽  
Lily Puspa Dewi
Keyword(s):  
Fuzzy Sets ◽  
Knowledge Based

Granular Computing and Human-Centricity in Computational Intelligence

2012 ◽  
pp. 13-27
Author(s):  
Witold Pedrycz
Keyword(s):  
Fuzzy Sets ◽  
Granular Computing ◽  
Knowledge Generation ◽  
Qualitative Assessment ◽  
Information Granules ◽  
Knowledge Based ◽  
Performance Indexes ◽  
Numeric Data ◽  
Type Information

Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity. This facet implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans. Systems that commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures, is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of this study. The author elaborates on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, the paper concentrates on knowledge-based clustering. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. Other interesting directions enhancing human centricity of computing with fuzzy sets deals with non-numeric semi-qualitative characterization of information granules, as well as inherent evolving capabilities of associated human-centric systems. The author discusses a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulates a series of associated optimization tasks guided by well-formulated performance indexes, and discusses the underlying essence of resulting solutions.

Piecewise linear reliability data analysis with fuzzy sets

2001 ◽  
Vol 215 (9) ◽  
pp. 1075-1082 ◽  
Author(s):  
J Harris
Keyword(s):  
Data Analysis ◽  
Fuzzy Sets ◽  
Piecewise Linear ◽  
Reliability Function ◽  
Graphical Analysis ◽  
Reliability Data ◽  
Knowledge Based ◽  
Form Part ◽  
Rate Functions ◽  
Insight Into

The Weibull three-parameter model is widely used in the analysis of reliability data, since it provides a good fit to many, but not all, data cases. In this work an analysis is presented in which locally constant hazard rate functions are applied in the analysis. By defining concomitant fuzzy reliability functions as overlapping fuzzy sets, the limited ranges may be conjoined to provide an extensive continuous reliability function. The efficacy of this treatment is tested with three different typical cases of published physical data that were previously the subjects of Weibull graphical analysis. It is found that in each case the treatment is effective. Different phases of the failure pattern of each case are identified and characterized by ‘time’ constants. The treatment is not limited to three parameters, as in the Weibull case, and it is therefore more flexible. It provides a deeper insight into the underlying failure mechanisms and may be expected to provide a wider range of applications, both within reliability data analysis and in other fields, and can form part of a knowledge-based system.

Granular Computing and Human-Centricity in Computational Intelligence

2012 ◽  
pp. 1721-1735
Author(s):  
Witold Pedrycz
Keyword(s):  
Fuzzy Sets ◽  
Granular Computing ◽  
Knowledge Generation ◽  
Qualitative Assessment ◽  
Information Granules ◽  
Knowledge Based ◽  
Performance Indexes ◽  
Numeric Data ◽  
Type Information

Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity. This facet implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans. Systems that commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures, is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of this study. The author elaborates on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, the paper concentrates on knowledge-based clustering. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. Other interesting directions enhancing human centricity of computing with fuzzy sets deals with non-numeric semi-qualitative characterization of information granules, as well as inherent evolving capabilities of associated human-centric systems. The author discusses a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulates a series of associated optimization tasks guided by well-formulated performance indexes, and discusses the underlying essence of resulting solutions.

Process Modelling and Formulation for the Development of an Intelligent Set-up Planning System

1995 ◽  
Vol 209 (6) ◽  
pp. 455-467 ◽  
Author(s):  
S K Ong ◽  
A Y C Nee
Keyword(s):  
Problem Solving ◽  
Knowledge Acquisition ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Planning System ◽  
Production Rules ◽  
Knowledge Based ◽  
Control Framework ◽  
Thinking Processes ◽  
Set Up

A set-up is a group of features that are machined together in one particular orientation and position of a workpiece. Hence, the task of set-up planning is to group the features that are required on a part into set-ups and sequence these resultant groups to form an optimally ordered plan to produce the part. This ordered plan has forthright effects on the process and fixture plans that are needed to produce this part. This paper presents the knowledge modelling and formulation process for the development of an intelligent set-up planning system. The knowledge-based information needed for set-up planning from experts in the machining domain and the problem-solving procedures of these experts were obtained from a knowledge acquisition process and modelled in this set-up planning system. The acquired knowledge is modelled using production rules and fuzzy sets, and is coupled with a fuzzy-set-based formulation of the problem-solving procedures of these experts. This formulation and a control framework for set-up planning that closely emulates the expert's thinking processes during planning are integrated to formulate set-up plans.

scholarly journals A Logic Framework for Non-Conscious Reasoning

2017 ◽  
Author(s):  
Felipe Lara-Rosano
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Fuzzy Sets ◽  
Object Oriented ◽  
Knowledge Based Systems ◽  
Multivalued Logic ◽  
Knowledge Based ◽  
Problem Solving Process ◽  
Everyday Problems

Human non-conscious reasoning is one of the most successful procedures developed to solve everyday problems in an efficient way. This is why the field of artificial intelligence should analyze, formalize and emulate the multiple ways of non-conscious reasoning with the purpose of applying them in knowledge based systems, neurocomputers and similar devices for aiding people in the problem-solving process. In this paper, a framework for those non-conscious ways of reasoning is presented based on object-oriented representations, fuzzy sets and multivalued logic.

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