Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model

Area of Science:

• Urban Planning
• Computational Social Science
• Artificial Intelligence

Background:

• Urban cultural space layout optimization is a complex, multi-objective challenge.
• Existing methods struggle with the dynamic and restricted nature of urban development.
• There is an urgent need for effective decision-making tools in urban cultural space planning.

Purpose of the Study:

• To develop and evaluate an optimization model for urban cultural space construction and layout.
• To analyze the feasibility of integrating a forward three-layer neural network with cultural space layout adjustments.
• To compare the proposed model with system dynamics (SD) for computer dynamic simulation.

Main Methods:

• Constructed an optimization model based on forward three-layer neural network theory.
• Analyzed the feasibility of combining neural networks with cultural space layout optimization.
• Performed dynamic simulation using the forward three-layer neural network model and compared it with the system dynamics (SD) model.
• Evaluated model performance based on feasibility and calculation error.

Main Results:

• The optimization model demonstrated good feasibility (0.897) and a low calculation error (6.21%).
• Simulations indicated an increasing trend in the number and expansion speed of cultural spaces.
• The model facilitated an average annual expansion of approximately 35 km² of cultural space.
• The study effectively enhanced the quality of regional planning and construction layout.

Conclusions:

• The forward three-layer neural network model is a viable and effective tool for optimizing urban cultural space layout.
• The proposed model provides accurate dynamic simulations for urban planning decisions.
• Implementation of the model leads to improved regional planning and increased cultural space development.