1. Analysis and thinking of question A
(Share on CSDN as soon as the competition question comes out)
3. Three models commonly used in previous A questions
3.1 Prediction Model
Prediction model:
- Neural Network Prediction,
- gray forecast,
- fit interpolated predictions (linear regression),
- time series forecasting,
- Markov chain prediction,
- Differential Equation Prediction,
- Logistic models and more.
Application areas: population forecast, water resource pollution growth forecast, virus spread forecast, competition winning probability forecast, monthly income forecast, sales forecast, economic development forecast, etc. in industrial, agricultural, commercial and other economic fields, as well as environmental, social and military, etc. There is a wide range of applications in the field.
Predictive Modeling: Moderate difficulty.
Fitted Interpolation Forecast: The basics are simple and easy to understand.
Fitting algorithm: matlab fitting toolbox, accurate...
Interpolation algorithm: short-term forecasting, complete data completion, interpolation function, Lagrangian interpolation method, cubic spline interpolation method...
Neural Network Prediction: Modern Optimization Algorithms, Test Programming Ability.
Population Forecast: Gray Forecast, Logistic Model…
3.2 Optimization model
Optimization model:
- programming models (objective programming, linear programming, nonlinear programming, integer programming, dynamic programming),
- graph theory model,
- queuing model,
- neural network model,
- Modern optimization algorithms (genetic algorithm, simulated annealing algorithm, ant colony algorithm, tabu search algorithm), etc.
Application fields: the shortest path problem of courier delivery, water resource scheduling optimization problem, toll station problem at highway intersection, timing and route selection for military operations to avoid air reconnaissance, logistics location problem, business district layout planning and other fields.
Optimizing the model: Difficult.
Cut lumber, flooring with minimum waste and maximum profit.
Natural water pipeline laying problem: graph theory model (Dijkstra algorithm Dijkstra, Kruskal algorithm Kruskal)
3.3 Evaluation Model
Evaluation model:
- fuzzy comprehensive evaluation method,
- analytic hierarchy process,
- cluster analysis,
- principal component analysis evaluation method,
- gray comprehensive evaluation method,
- Artificial neural network evaluation method and so on.
Application areas: water resource evaluation in a certain area, risk evaluation of water conservancy projects, urban development degree evaluation, football coach evaluation, basketball team evaluation, water ecological evaluation, dam safety evaluation, slope stability evaluation.
Predictive Model: Simple.