# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 3 System Modeling Method Construction  
### Support Vector Machine SVM Model Method  
Support vector machines (SVMs) are a class of generalized linear classifiers that perform binary classification of data in a supervised learning manner. The decision boundary is the maximum-margin hyperplane solved from the learning samples, transforming the problem into one of solving a convex quadratic programming issue [21; 22]. The performance of SVMs crucially depends on the choice of the kernel function method. The main kernel functions include linear, polynomial, Sigmoid, and Gaussian radial basis function (RBF) [23]. The kernel function calculation formula is as follows:  
\[\mathrm{K}(\mathrm{X},\mathrm{Z})=\mathcal{O}(\mathrm{x})\mathcal{O}(\mathrm{ z}) \tag{1}\]  
Formula (1) represents the calculation of the inner product directly in the feature space, and \(\mathcal{O}\) represents the process of mapping \(\mathrm{x}\) to the inner product feature space. (1) Linear Kernel. \[\mathrm{K}(\mathrm{x}_{i},\mathrm{x})=\mathrm{x}_{i}^{\mathrm{T}}\mathrm{x} \tag{2}\]  
Formula (2) represents the inner product calculation in the original space of the data, functioning to align the data forms of the two spaces. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Partial Data Related to Long-Grain Rice at 30 \({}^{\circ}\)C**} \\ \hline \hline \multirow{2}{*}{**Resistance Value (K)**} & **True Voltage Value** & **Actual Voltage Value** & **Measured Moisture** \\  & **(V)** & **(V)** & **Value (\%)** \\ \hline
26,000 & 1.2476 & 1.2481 & 13.3 \\
8000 & 1.3819 & 1.3812 & 14.6 \\
1200 & 1.6145 & 1.6153 & 18.1 \\
460 & 1.7302 & 1.7291 & 19.6 \\
110 & 1.8699 & 1.8705 & 24.1 \\
61 & 1.9220 & 1.9221 & 25.6 \\
16 & 2.0187 & 2.0194 & 30.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of data processing section. Figure 1: Data cleaning box diagram. (2) Polynomial Kernel. \[K(x_{i},x)=\left(\gamma x_{i}^{T}x+r\right)^{d} \tag{3}\]  
The polynomial kernel focuses on the global nature of the data. Direct calculation in polynomial space would lead to the curse of dimensionality, hence it includes a transformation process from high-dimensional space to low-dimensional space, where the inner product is calculated using the latter. (3) Sigmoid Kernel. \[K(x_{i},x)=\tanh\left(\gamma x_{i}^{T}x+r\right) \tag{4}\]  
The Sigmoid kernel approximates a multilayer perceptron neural network and focuses on the overall optimal value of the sample data. (4) Gaussian radial basis kernel (RBF). \[K(x_{i},x)=\exp\left(-\gamma||x_{i}-x||^{2}\right) \tag{5}\]  
Formula (5) transforms the original space into a feature space of infinite dimensions. Its effectiveness relies on the regulation of parameters \(\gamma\), which possess significant adjustability, thereby making it extensively utilized in practical applications. The importance of selecting a kernel function for the performance of SVMs is as follows:  
(1) The kernel function defines the feature space: The kernel function determines how the data are mapped to a new feature space. Different kernel functions create different feature spaces. (2) Curse of dimensionality: using a kernel function allows us to compute dot products in this high-dimensional space without explicitly mapping the data, thereby avoiding the computational and storage issues associated with the curse of dimensionality. (3) Generalization ability of the model: a suitable kernel function can help the model learn more complex decision boundaries, thus leading to better generalization performance on unseen data. (4) Complexity of the optimization problem: the choice of kernel function directly affects the complexity of the optimization problem in SVM. (5) Data adaptability: different datasets may require different types of kernel functions. Support Vector Regression (SVR) is a regression method based on the principles of support vector machines, used for predicting continuous numerical values.