## 2 Related Work
### Subtractive Clustering Method  
Clustering is an unsupervised learning algorithm that learns data without labels. Therefore, the goal of unsupervised learning is to reveal the intrinsic properties and patterns of data by learning from unlabeled samples. Clustering partitioning metrics include density and distance [27], and there are many methods for distance partitioning, such as Euclidean distance, Manhattan distance, cosine distance, and Hamming distance. Subtrac \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **Neural Networks** & **Fuzzy Logic** & **ANFIS** \\ \hline Basic Component & Neurons & Expert knowledge and rules & Neurons and Fuzzy Inference Units \\ Knowledge Acquisition & Samples and algorithm examples & Logical reasoning & Samples and expert knowledge \\ Inference Operation & Connection of neurons & Combination of fuzzy rules and heuristic search [17] & Membership functions and fuzzy inference \\ Representation & Distributed representation & Membership function & Membership functions and weight coefficients \\ Inference Effect & Weighted sum of neurons [16] & Max-min of membership function & Weighted sum of membership functions and weight coefficients \\ Adaptive & Learning by adjusting weights, high fault tolerance, and strong generalization ability & Inductive learning and low error tolerance & Adaptive hybrid of learning and fuzzy inference with high fault tolerance [20] \\ Advantages & Self-learning self-organization ability, high fault tolerance, strong generalization ability & Expert experience can be used, with high accuracy and interpretability & Combining the advantages of neural networks and fuzzy logic systems, it can not only deal with fuzzy problems but also learn adaptively \\ Disadvantage & Black box model, difficult to express knowledge, and difficult to understand and explain & The reasoning process is highly ambiguous, the learning and reasoning process is complex and requires considerable computing resources \\  & & Deaths with problems with high ambiguity, uncertainty, and complexity, such as control systems, decision support systems, and intelligent transportation systems & Can be applied in various fields, such as prediction, control, and classification \\ Application Scenario & Pattern recognition, classification, prediction, and other problems that require high generalization ability & Deals with high ambiguity, uncertainty, and complexity, such as control systems, decision support systems, and intelligent transportation systems & Can be applied in various fields, such as prediction, control, and classification \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of neural networks, fuzzy logic system, and ANFIS technology. tive clustering belongs is one type of density clustering [28], and the complexity of the clustering algorithm is generally linearly related to the dimensionality of the data. ANFIS partitioning refers to the division of the input space into several subspaces and the modeling of each subspace using the same fuzzy rules. ANFIS input space is generally partitioned using one of two methods: grid partitioning (GP) or the subtractive clustering method (SCM) [29]. In the GP method, the input space is uniformly divided into a series of small grids, and each grid corresponds to a fuzzy rule. During the training process, the parameters of each rule (including membership functions and output functions) are adjusted to minimize the loss function. The advantage of grid partitioning is that it is easy to understand. However, when processing high-dimensional input spaces, the number of grids grows exponentially, leading to low training efficiency. When the number of input features exceeds 5, the computational cost becomes excessively high. SCM is a distance-based clustering method, the main concept of which is to cluster based on the distances between input data. SCM first calculates the distance between each input sample, then aggregates the samples that are close in distance based on a threshold to obtain several clustering centers.