Humidity and temperature control are indispensable for human ease, and also for storage purposes. In this chapter, the authors discuss how fuzzy logic is used to regulate the environment's temperature and humidity. For the implementation the authors require some linguistic variables to express the temperature or humidity and fuzzy sets are used to define the membership functions for the used linguistic variable. Eventually, a fuzzy inference system will set up a relation between input and output variables. Here, kingpins are the temperature and humidity, so the input variables are: cool, cold, warm, hot; and output variables are: low, high. Then fuzzy rules are established to get the relation that will help regulate the humidity and temperature. This type of product can be manufactured in the field of production for the well-being of people who are facing where the humidity is high compared to heat.
Top1. Introduction
Fuzzy logic is a mathematical and computational framework that deals with uncertainty and imprecision in decision-making and problem-solving. It is a multi-valued logic system that allows for degrees of truth, in contrast to classical binary logic, which operates with only two truth values, true and false (Li et al., 2019). Fuzzy logic is particularly used in situations where information is vague, ambiguous, or incomplete. Lotfi A. Zadeh introduced the concept of fuzzy logic and has found applications in various fields, including control systems, artificial intelligence, and decision support systems. Fuzzy logic enables computers and machines to work with concepts that have no fixed boundaries, making it well-suited for modelling human-like decision processes (Islam et al., 2021).
In fuzzy logic, variables can take on values between 0 and 1, representing the degree to which a statement is true or false. This approach allows for a more nuanced and realistic representation of the real world, where things are often not simply black or white but exist in shades of grey. Fuzzy logic uses membership functions to define the degree of membership of a value to a particular set or category (Becker et al., 1994). Key components of fuzzy logic include linguistic variables, fuzzy sets, and fuzzy rules. Linguistic variables are used to express qualitative concepts, and fuzzy sets define the membership functions for these variables.
Fuzzy rules are employed to establish relationships between the input variables and output variables, allowing for the creation of fuzzy inference systems (Niu et al., 2002). Fuzzy logic has been applied in various applications, such as controlling industrial processes, optimising traffic signal timing, developing expert systems, and making decisions in fields like finance and medicine. Its ability to handle uncertainty and imprecision makes it a valuable tool for tackling complex real-world problems. In this paper, we consider a simple example involving a fuzzy temperature control system for a heating device, such as a space heater (Thomas, 2010) (Khang, 2023).
In this scenario, we will use fuzzy logic to control the heater's output based on the current room temperature. Some of the variables used for identifying fuzzy have been given as input variables Fuzzy Variables: Input Variable: Temperature. Linguistic Terms: Cold, Cool, Warm, Hot. Membership Functions: Each linguistic term has a membership function that assigns a degree of truth to the term. For example, “Cold” might have a membership function that peaks at a low temperature, gradually decreasing as the temperature increases (Islam et al., 2006). Output Variable: Heater Power. Linguistic Terms: Low, Medium, High. Membership Functions: Similar to the input variable, the output variable has linguistic terms with associated membership functions (García Arenas, 2010).