Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Is there a need for fuzzy logic?
Information Sciences: an International Journal
A reduction approach for fuzzy rule bases of fuzzy controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reduction of fuzzy rule base via singular value decomposition
IEEE Transactions on Fuzzy Systems
On stability of fuzzy systems expressed by fuzzy rules with singleton consequents
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Network-based fire-detection system via controller area network for smart home automation
IEEE Transactions on Consumer Electronics
On the Equivalence Conditions of Fuzzy Inference Methods—Part 1: Basic Concept and Definition
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
This paper presents a novel fuzzy deterministic noncontroller type (FDNCT) system and an FDNCT inference algorithm (FIA). The FDNCT uses fuzzy inputs and produces a deterministic non-fuzzy output. The FDNCT is an extension and alternative for the existing fuzzy singleton inference algorithm. The research described in this paper applies FDNCT to build an architecture for an intelligent system to detect and to eliminate potential fires in the engine and battery compartments of a hybrid electric vehicle. The fuzzy inputs consist of sensor data from the engine and battery compartments, namely, temperature, moisture, and voltage and current of the battery. The system synthesizes the data and detects potential fires, takes actions for eliminating the hazard, and notifies the passengers about the potential fire using an audible alarm. This paper also presents the computer simulation results of the comparison between the FIA and singleton inference algorithms for detecting potential fires and determining the actions for eliminating them.