Neural networks, fuzzy logic, and expert systems are fundamental to the development of intelligent systems capable of addressing complex decision-making challenges across various domains. Neural networks, inspired by the structure of the human brain, demonstrate proficiency in pattern recognition, data classification, and high-accuracy prediction. Fuzzy logic facilitates reasoning under uncertainty, enabling systems to process imprecise inputs and generate responses that resemble human reasoning. Expert systems employ rule-based reasoning to emulate expert decision-making, delivering reliable solutions across healthcare, diagnostics, and industrial automation. This paper examines the underlying principles, strengths, limitations, and applications of these three artificial intelligence techniques. Through comparative analysis, it highlights their performance distinctions and unique contributions to intelligent problem-solving. Additionally, the study investigates the advantages of integrating these methods to create hybrid intelligent systems with improved adaptability, accuracy, and reliability. Such integrated approaches have the potential to advance AI-driven solutions in smart systems, real-time monitoring, and automated decision support.
Chitte et al. (Thu,) studied this question.
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