## 2 Development of a Servo Valve Prediction Model
### Support Vector Machines (SVM)  
The basis for support vector machines (SVMs) was developed by [PERSON] (1995) and they are widely used due to their many advantageous features and empirical nature. The method has structural risk minimization (SRM) properties that are more advanced than the traditional empirical risk minimization (ERM) principles used in neural networks. SRM minimizes the upper bound on the expected risk, while ERM minimizes the error in the training data. The differences give the SVM more excellent generalization capability, which is the goal of statistical learning. Support vector machines were developed for solvingclassification problems, but recently they have been extended to the field of regression problems. In the literature, the terminology of SVMs can be confusing. The term SVM allows for a concise classification of support vector methods, and support vector regression covers the regression of support vector methods. Throughout this paper the term SVM will refer to classification and regression methods. Support vector classification (SVC) and Support vector regression (SVR) will be used to specify the minimization principle. By definition, support vector machines are divided into support vector classification machines and support vector regression machines. Support vector classifier is used to study the quantitative relationship between input variables and dichotomous variables and classification prediction. It takes the training set as the data object, analyzes the quantitative relationship between input variables and dichotomous output variables, and implements the prediction of the output variable class for new data. Support vector regression machines are used to study the quantitative relationship between input variables and numerical output variables and regression prediction. It also takes the training set as the data object and finds the maximum bounded regression plane by analyzing the quantitative relationship between input variables and numerical output variables to achieve a robust prediction of the value of the newly observed output variables. Support vector machines are an extension of algorithms based on the statistical learning theory that includes the VC dimensional theory and structural risk minimization principles and is a supervised learning method. Compared to other neural network regressions, the use of support vector machines to estimate regression functions has three distinct features. Firstly, the SVM estimates the regression using a linear part of special attributes in a high-dimensional space. Secondly, the SVM completes the regression estimation by risk reduction, wherein the risk is evaluated using Vapnik's insensitive loss function. Thirdly, the principle of the set of functions used by support vector machines is that the composition of regularization terms derived from empirical error and structural risk minimization must be followed. Support vector machines (SVMs) overcome the inherent flaws of neural networks, such as local minima, over-learning and architectural selection, and over-reliance on experience for types. In its simplest and most linear form, an SVM is a hyperplane that divides a set of positive samples from a collection of negative samples with maximum margins. In the attribute-deterministic case, the margin is defined by the distance from the hyperplane to the nearest positive and negative sample. A decision tree is a binary tree with two types of nodes. Some nodes are leaf nodes; they contain class labels or predictions. Other nodes are internal; they have functions for splitting the original sample. In internal nodes, attributes are often compared to a specific value, which implies using simple threshold rules. However, this does not exclude the possibility of using complex multidimensional functions. These functions help to construct complex partition surfaces, but they are rarely used in practice because they make decision trees relearn more quickly.