Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Default knowledge and measures of specificity
Information Sciences: an International Journal
A review and comparison of six reasoning methods
Fuzzy Sets and Systems
Fuzzy Sets and Systems
A course in fuzzy systems and control
A course in fuzzy systems and control
Approximate reasoning an evidence theory
Information Sciences: an International Journal
A new interpolative reasoning method in sparse rule-based systems
Fuzzy Sets and Systems
Fuzzy Sets and Systems
An overview of fuzzy quantifiers (II). Reasoning and applications
Fuzzy Sets and Systems
On the logic foundation of fuzzy reasoning
Information Sciences: an International Journal
Optimal fuzzy reasoning and its robustness analysis: Research Articles
International Journal of Intelligent Systems - Intelligent and Soft Computing Techniques for Information Processing
Planning Algorithms
Variable weighted synthesis inference method for fuzzy reasoning and fuzzy systems
Computers & Mathematics with Applications
Real-time path planning with limited information for autonomous unmanned air vehicles
Automatica (Journal of IFAC)
Fuzzy reasoning based on a new fuzzy rough set and its application to scheduling problems
Computers & Mathematics with Applications
An improved robust fuzzy-PID controller with optimal fuzzy reasoning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robustness of fuzzy reasoning and δ-equalities of fuzzy sets
IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making
IEEE Transactions on Fuzzy Systems
Fuzzy Reasoning as a Control Problem
IEEE Transactions on Fuzzy Systems
Bi-level programming based real-time path planning for unmanned aerial vehicles
Knowledge-Based Systems
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Fuzzy reasoning methods are extensively used in intelligent systems and fuzzy control. In our previous work, an explicit feedback mechanism is embedded into optimal fuzzy reasoning methods, and the resulting fuzzy reasoning methods are more robust than the optimal fuzzy reasoning methods. An interesting question is that, without the introduction of optimization goals, can the robustness of fuzzy reasoning methods be improved by embedding feedback mechanisms? This paper is intended to answer the question. By embedding feedback mechanisms into CRI approach, a new feedback based CRI (FBCRI) approach is proposed and three implementation methods with different strategies are obtained. Simulation results show that the feedback mechanisms are really useful for the improvement of the robustness of CRI methods. At last, to test the applicability of the proposed approach, it is applied to the solution of a real-time path planning problem for UAVs. The effectiveness and efficiency of an FBCRI based real-time path planning algorithm are verified by representative test examples, which show that embedding feedback information into the fuzzy reasoning process actually improve the quality of flight paths.