Fuzzy Applications in the Decision Models and Expert Systems for Control Capability Enhancement
Gourab Dutta, Reshmi Ghosh, Gunjan Mukherjee · Apple Academic Press eBooks · 2025
Fuzzy logic has emerged as a powerful paradigm with an approach to handling the inherent uncertainties and impressions of real-world data. In decision models, this property enables the incorporation of qualitative and quantitative data with varying degrees of certainty. Applications of fuzzy logic are manifold. In medical diagnosis, it aids in interpreting vague symptoms and assessing diagnostic probabilities, enhancing the accuracy of healthcare decision support systems. In financial modeling, empowers risk assessment by accommodating fluctuating market conditions and imprecise economic data. This concept has been used in shaping the environmental models by handling incomplete and uncertain ecological data. In control systems, fuzzy logic controllers excel at managing complex, nonlinear processes, and finding applications in robotics, manufacturing, and process control. Fuzzy logic concept in the pattern recognition systems can be concerned to the image recognition and natural language processing. The quality control and fault detection systems employ fuzzy logic to evaluate product quality using imprecise measurements. The optimization of traffic signal timing using fuzzy logic system is another control instance. The proposed chapter will provide detailed anecdote over the applicability of fuzzy system into various decision models and expert systems with consequent enhancement of efficiency and accuracy.