Fuzzy logic techniques are efficient in solving complex, ill-defined problems that are characterized by uncertainty of environment and fuzziness of information. Fuzzy logic allows handling uncertain and imprecise knowledge and provides a powerful framework for reasoning. The inference technique of fuzzy logic resembles human reasoning capabilities. Fuzzy reasoning models are relevant to a wide variety of subject areas such as engineering, economics, psychology, sociology, finance, and education. In the literatures various fuzzy reasoning methods are purposed to process uncertain information and increase the efficiency of the designed systems. These fuzzy reasoning methods are mainly based on compositional rule, analogy and similarity, interpolation, and the concept of distance. The speed, processing capabilities, and complexity of these reasoning methods are important issues.

Different systems based on type 1 fuzzy sets, type 2 fuzzy sets, and Z numbers use various fuzzy reasoning algorithms for processing uncertain information. The designing of efficient fuzzy reasoning mechanisms for these systems is becoming very important issue. Hereby it is necessary to consider the latest trends and developments in the advanced fuzzy systems and to develop efficient reasoning mechanisms for solving practical problems.

The special issue is aimed at bringing forward well-focused and comprehensive papers in the theory and applications of fuzzy reasoning methods. We are particularly interested in articles describing the new structures, algorithms, and advances in the design of fuzzy reasoning mechanisms for type 2 fuzzy systems and systems based on Z numbers. Papers published in this issue will bring together outstanding research and developments of fuzzy systems.

Potential topics include, but are not limited to:

• Fuzzy reasoning under uncertainty
• Decision making and decision support systems based on fuzzy reasoning methods
• Type 2 fuzzy systems and reasoning techniques
• Z number based reasoning techniques
• Hybrid reasoning based on fuzzy sets, neural networks, and evolutionary algorithms
• Practical application of fuzzy reasoning models in engineering, economics, finance, sociology, and education