IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Formal Rules for Fuzzy Causal Analyses and Fuzzy Inferences

Formal Rules for Fuzzy Causal Analyses and Fuzzy Inferences
View Sample PDF
Author(s): Yingxu Wang (International Institute of Cognitive Informatics and Cognitive Computing (ICIC), University of Calgary, Calgary, Canada)
Copyright: 2012
Volume: 4
Issue: 4
Pages: 17
Source title: International Journal of Software Science and Computational Intelligence (IJSSCI)
DOI: 10.4018/jssci.2012100105

Purchase

View Formal Rules for Fuzzy Causal Analyses and Fuzzy Inferences on the publisher's website for pricing and purchasing information.

Abstract

Causal inference is one of the central capabilities of the natural intelligence that plays a crucial role in thinking, perception, and problem solving. Fuzzy inferences are an extended form of formal inferences that provide a denotational mathematical means for rigorously dealing with degrees of matters, uncertainties, and vague semantics of linguistic variables, as well as for rational reasoning the semantics of fuzzy causalities. This paper presents a set of formal rules for causal analyses and fuzzy inferences such as those of deductive, inductive, abductive, and analogical inferences. Rules and methodologies for each of the fuzzy inferences are formally modeled and illustrated with real-world examples and cases of applications. The formalization of fuzzy inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, soft computing, abstract intelligence, and computational intelligence.

Related Content

Unobtrusive Academic Emotion Recognition Based on Facial Expression Using RGB-D Camera Using Adaptive-Network-Based Fuzzy Inference System (ANFIS)
James Purnama, Riri Fitri Sari. © 2019. 15 pages.
View Details View Details PDF Full Text View Sample PDF
Evaluating the Effects of Size and Precision of Training Data on ANN Training Performance for the Prediction of Chaotic Time Series Patterns
Lei Zhang. © 2019. 15 pages.
View Details View Details PDF Full Text View Sample PDF
Test Suite Optimization Using Firefly and Genetic Algorithm
Abhishek Pandey, Soumya Banerjee. © 2019. 16 pages.
View Details View Details PDF Full Text View Sample PDF
Using Vehicles as Fog Infrastructures for Transportation Cyber-Physical Systems (T-CPS): Fog Computing for Vehicular Networks
Md Muzakkir Hussain, M.M. S Beg. © 2019. 23 pages.
View Details View Details PDF Full Text View Sample PDF
A Novel Chaotic Northern Bald Ibis Optimization Algorithm for Solving Different Cluster Problems [ICCICC18 #155]
Ravi Kumar Saidala, Nagaraju Devarakonda. © 2019. 25 pages.
View Details View Details PDF Full Text View Sample PDF
Safe-Platoon: A Formal Model for Safety Evaluation
Mohamed Garoui. © 2019. 12 pages.
View Details View Details PDF Full Text View Sample PDF
A Novel Convolutional Neural Network Based Localization System for Monocular Images
Chen Sun, Chunping Li, Yan Zhu. © 2019. 13 pages.
View Details View Details PDF Full Text View Sample PDF
Body Bottom