Mathematical Modelling and Grand Nain

Mathematical modeling was performed to predict the quality of banana fruit varieties. Grand Nain statistically analyzes the properties of the observed banana based on physical properties such as length, width, thickness, volume, geometric mean diameter, and calculates the mean, standard deviation, error, and dispersion coefficient I ask. Fit models are divided into three categories. In the first classification model, the empirical formula describing length and width (Model 4) predicts the mass with the largest R2 value.

The mathematical model uses a mathematical language to describe the system. The process of developing a mathematical model is called "mathematical modeling" (also modeling). Eykhoff (1974) defines a mathematical model as "a representation of the fundamental aspect of an existing system (or a constructed system) that presents system knowledge in a usable form". Mathematical models take various forms, such as dynamic systems, statistical models, differential equations, and game theory models. These and other types of models may overlap, and certain models include various abstract structures. A system is a group of entities that form an entire, interactive or interdependent entity or abstract. A power system modeled as mathematically formulated has a fixed "rule" that describes the time dependence of the position of points in its surrounding space. A slight change in system state corresponds to a small change in number.

Mathematical Modeling and Mathematics Practice Standard 2 (SMP 2): The reasons for abstraction and quantification are closely related. SMP 2 emphasizes abstraction, such as building various kinds of mathematical expressions, understanding of these expressions, and establishing connections between representations. On the other hand, SMP 4 emphasizes the use of abstract representation in mathematical modeling and the link between real situation and mathematical expression to simulate these situations.

The model or mathematical model uses a mathematical structure (equation, graph, chart (Ven map, pie chart, etc.)) to represent the actual scenario. It is an equation or graph that transforms the representation of the actual scene into a formula. Deep learning uses artificial neural networks with multiple layers to learn the best model parameters using special intelligence techniques. With the best model parameters, we can get better models to achieve very accurate decisions, but these mathematical calculations or representations of calculations are very complex and require strict It requires processing power.