Fuzzy Neural Networks: Simulation and Applications

In this article, we explore simulation techniques based on fuzzy neural networks and their applications across various domains. We begin by introducing the fundamental principles and architecture of fuzzy neural networks, along with implementation approaches for simulation using this technology. Key algorithmic components typically include fuzzy rule base initialization through clustering methods like FCM (Fuzzy C-Means), neural network weight optimization using backpropagation, and hybrid learning mechanisms that combine gradient descent with least-squares estimation for parameter tuning.

Subsequently, we examine practical applications in control systems (e.g., adaptive PID controllers with fuzzy neural compensation), robotics (path planning with fuzzy logic-driven neural decision networks), medical image processing (pattern recognition for diagnostic imaging using fuzzy feature extraction layers), and other fields. The discussion extends to future development trends, including deep fuzzy neural architectures and real-time embedded implementations. We anticipate this technology will play an increasingly vital role in scientific research and engineering practices, contributing significantly to societal advancement through enhanced pattern recognition capabilities, adaptive control precision, and robust uncertainty handling in complex systems.