1. System model is a description of the essential attributes of the system, which provides knowledge about the system in certain forms (such as words, symbols, charts, objects, mathematical formulas, etc.). ). The system model is generally not the system object itself, but the description, imitation and abstraction of the real system. For example, a globe is an approximate or concentrated reflection of the essence and characteristics of the prototype of the earth.
2. Physical model refers to the intuitive expression of the characteristics of cognitive objects in the form of objects or pictures. Physical model is a simplified and generalized description of cognitive objects for specific purposes, which can be qualitative or quantitative; Some are expressed by concrete objects or other visual means, while others are expressed by abstract forms.
3. Mathematical model is a scientific or engineering model constructed by using mathematical logic method and mathematical language. The history of mathematical models can be traced back to the time when humans began to use numbers. With the application of numbers, various mathematical models are constantly established to solve various practical problems.
Related knowledge of establishing mathematical model
1. Building a digital model is a process that requires careful thinking and scientific methods. First of all, we should make clear what the problem needs to be solved. This helps to determine the goal and scope of the model. Collect data related to the problem. These data can be existing data sets or collected through experiments or surveys. Ensure the accuracy and reliability of data.
2, determine the variables, determine the variables related to the problem. These variables can be input and output variables or intermediate variables. Using appropriate mathematical tools and techniques, the relationship between variables is expressed by mathematical equations or models. This can be linear regression, logical regression, neural network and other models.
3. Verifying the model is an important step to evaluate the model performance. It is necessary to select appropriate evaluation indexes and verification methods, adjust and optimize the model according to the verification results, and train and verify the model with known data. By comparing the predicted results with the actual results, the accuracy and reliability of the model are evaluated.