RTT Intelligent Prediction Algorithm Based on Fuzzy Neural Network

**Introduction to Fuzzy Neural Network** Fuzzy neural networks are a powerful integration of fuzzy logic and artificial neural networks. By combining the learning capabilities of neural networks with the flexibility of fuzzy logic, they offer a robust approach for handling uncertainty, making them ideal for complex systems where precision and adaptability are essential. This hybrid system is particularly useful in environments where traditional control methods struggle due to nonlinearities, imprecision, or dynamic changes. One of the key challenges in system design is balancing complexity with accuracy. To address this, researchers have turned to intelligent control strategies that mimic human learning and adaptation. In 1991, control expert Aström highlighted at the IFAC conference that fuzzy logic control, neural networks, and expert systems are three major approaches in intelligent control. However, expert systems rely heavily on domain knowledge and may not capture the inherent uncertainties of real-world systems. This limitation makes it difficult to build accurate models for complex processes. Fuzzy logic and neural networks each have their own strengths and weaknesses. Fuzzy logic excels at handling imprecise data and making decisions based on linguistic rules, while neural networks are excellent at learning from data and adapting over time. The fusion of these two—fuzzy neural networks—combines their advantages while mitigating their drawbacks. This synergy has made fuzzy neural networks a focal point in modern intelligent control research. In addition to fuzzy logic and neural networks, other techniques such as genetic algorithms, stochastic reasoning, and chaos theory also contribute to the development of hybrid intelligent systems. These methods do not compete but rather complement each other, working together to solve complex problems more effectively. By integrating multiple approaches, a more robust and adaptive system can be created, capable of handling a wide range of scenarios. As a critical parameter in network congestion control, Round-Trip Time (RTT) plays a vital role in assessing network performance. RTT reflects network congestion earlier than packet loss rate, making it a more sensitive indicator. Research [1] introduced an RTT-driven congestion control algorithm, which showed significant improvements in real-time performance and reduced network state oscillations compared to traditional methods based on packet loss. To estimate RTT, formula (1) is commonly used: **RTTn+1 = RTTn + g × E (E = RTTm - RTTn)** Where RTTm represents the current measured RTT value, RTTn is the previous average RTT estimate, and g is a smoothing factor between 0 and 1. The choice of g depends on the network environment and varies across different networks or time slots. For reliable multicast transmission, an active network-based RTT estimation strategy was proposed in literature [2], enabling efficient reduction of unnecessary control information and improving multicast throughput. RTT prediction remains a hot topic in network research, as accurate predictions can enhance network performance. Literature [3] applied a sliding window weighted average RTT estimation algorithm combined with waveform smoothing and mutation indices. While neural networks have shown promise in RTT prediction, their effectiveness is limited under high network load. When the network becomes congested, RTT estimates become less accurate due to increased queue delays, jitter, and packet loss, leading to instability. To address these challenges, this paper proposes a combination of low-pass filtering and MBP (Modified Backpropagation) networks. The RTT data exhibits strong high-frequency noise, especially in busy networks. By applying a low-pass filter to reduce noise and using an MBP network for prediction, the system achieves better results even in congested conditions. Experimental results demonstrate that this approach significantly improves RTT prediction accuracy in real-world network environments. **Network Round Trip Delay** The performance of network devices and the overall network environment greatly influences data throughput. As a result, network traffic often contains a lot of short-term, high-frequency noise, making it challenging to extract meaningful patterns. Since data packets can take different paths between nodes, the RTT can vary significantly depending on the route. Even when packets follow the same path, differences in network device loads can lead to variations in RTT values. Over longer periods, network conditions and device performance tend to stabilize. However, since network data is generated under both random and stable constraints, filtering is essential to identify underlying patterns. At this stage, the focus shifts from random fluctuations to the regular behavior of network nodes, making filtering a crucial step in analyzing network data. **RTT Data Preprocessing** The low-pass sliding filter algorithm works by taking a value between 0 and 1, denoted as 'a', and applying the following formula: **Filtering Result = (1 - a) × Current Sample Value + a × Previous Filtered Result** This method is effective at reducing periodic interference and is suitable for high-fluctuation environments. A value of a = 0.05 was selected for this study. An RTT test was conducted on a campus network, with source and feedback nodes located at Tianjin Vocational and Technical Normal University and Tianjin University of Technology, respectively. In the experiment, 200 TCP packets of 10 bytes were sent every 100 ms, and the round-trip time was calculated by measuring the difference between transmission and reception times. 

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