An Overview of Fuzzy Systems

Alonso, José M.; Castiello, Ciro; Magdalena, Luis; Mencar, Corrado

doi:10.1007/978-3-030-71098-9_2

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…Fuzzification is a crucial step in applying fuzzy logic to systems like a rose irrigation system. It involves the process of converting crisp, precise input values into fuzzy linguistic terms or labels, which represent the qualitative assessment of these values [10]. This step allows us to deal with imprecise or uncertain information regarding factors like soil moisture levels, weather conditions, and plant water requirements.…”

Section: ) Fuzzificationmentioning

confidence: 99%

“…The curves of these membership functions are crucial in defining the behavior of fuzzy rules and, subsequently, in making decisions within a fuzzy logic system. They serve as a bridge between crisp, numerical data and linguistic variables, enabling the system to process and respond to imprecise information in a meaningful way [10].…”

Section: ) Fuzzificationmentioning

confidence: 99%

“…These rules are typically expressed in the form of conditional statements that link linguistic variables. Each rule comprises two main parts: an antecedent (or premise) and a consequent [10].…”

Section: ) Fuzzy Rule Base (Knowledge Base)mentioning

confidence: 99%

“…Defuzzification is a crucial step in fuzzy logic systems where the final fuzzy output is transformed into a crisp, numerical value that can be used for practical control or decision-making. In other words, it converts a fuzzy set or fuzzy value into a specific, actionable response [10].…”

Section: ) Defuzzificationmentioning

confidence: 99%

“…numerator calculates the weighted sum of these fuzzy outputs, while the denominator normalizes the result by dividing by the total weight. This ensures that the final defuzzified value accurately reflects the overall contribution of the rules [10].…”

Section: ) Defuzzificationmentioning

confidence: 99%

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Fuzzy Logic Controller Design for Smart Watering System of Rose Cultivation

Arifin,

Mukti,

Aziz

2023

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The traditional methods of rose cultivation often rely on manual irrigation practices, which may not always be precise or efficient. Additionally, the cost associated with implementing automated irrigation systems has been a limiting factor for many farmers. This research addresses these challenges by exploring the integration of Fuzzy Logic Controller (FLC) technology and low-cost electronic devices to develop an automated irrigation system tailored for rose cultivation, aiming to enhance precision and accessibility in agricultural practices. The study demonstrates the effectiveness of this approach in optimizing watering practices, showcasing a notable level of accuracy in providing irrigation recommendations. Moreover, the implementation of low-cost electronic devices enhances the accessibility and feasibility of such smart irrigation systems. The research lays a foundation for advancements in precision agriculture, particularly in the domain of rose cultivation, with potential implications for broader agricultural practices.

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Section: ) Fuzzificationmentioning

confidence: 99%

Section: ) Fuzzificationmentioning

confidence: 99%

Section: ) Fuzzy Rule Base (Knowledge Base)mentioning

confidence: 99%

Section: ) Defuzzificationmentioning

confidence: 99%

Section: ) Defuzzificationmentioning

confidence: 99%

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