MATLAB Simulation Model of Parking Lot Based on Fuzzy Mathematics

A parking lot MATLAB simulation model based on fuzzy mathematics is an intelligent system that utilizes fuzzy logic to handle uncertainty and complexity. This model simulates variable factors in real parking scenarios (such as parking space distance, parking time preferences, etc.), converting traditional binary logic into continuous membership functions to achieve parking resource allocation that better aligns with human decision-making processes.

The core design approach consists of three layers: first, establishing a fuzzification interface for input variables to convert precise numerical values like parking space distance and turnover rate into linguistic variables such as "far/near" and "high/low"; second, constructing a fuzzy rule base using IF-THEN statements to describe expert knowledge; finally, generating optimal parking space recommendations through defuzzification. When implementing this model in MATLAB using the Fuzzy Logic Toolbox, key considerations include tuning the shape parameters of membership functions and adjusting rule weights through systematic parameter optimization.

The innovation of this model lies in its ability to handle conflicting requirements that are difficult to quantify using traditional algorithms, such as simultaneously optimizing for both shortest walking distance and closest exit proximity. Simulation experiments demonstrate that compared to fixed allocation strategies, fuzzy decision-making can reduce average parking search time by over 30%, making it particularly suitable for dynamic scheduling scenarios in large multi-story parking facilities. The implementation typically involves MATLAB's fuzzy inference system functions like fuzzy for system creation and evalfis for rule evaluation, with custom membership functions designed using trimf or gaussmf for precise control over fuzzy sets.