The paper describes the task to create a decision support system that allows you to determine the optimal composition of the team of performers. The sports version of the game “What? Where? When?" is chosen. The principles of holding tournaments on the intellectual game “What? Where? When?" and the rules for the formation of teams to participate in such competitions. It is concluded that to predict the impact of changes in the team composition on the result, it is advisable to use modern mathematical and intellectual methods, including the method of artificial neural networks. The available data on the results of synchronous tournaments LUK (ST) since 2011 and city tournaments (GT) since 2017 are presented, the main indicators for each competition are characterized. The introduction of the output factors is substantiated: the ratio of the team’s result to the average result and the ratio of the team’s result to the winner’s result. The forecasting problem is formulated as a prediction of the relative result of a team on a specific game based on the available list of team players for this game. It is proposed to take into account the location of a particular game, and fix the participation of players in the form of a “share” of the contribution to the team’s result, while the sum of the “shares” of all players should be equal to one. A method of artificial neural networks with a two-layer perceptron architecture, a sigmoid activation function and an error propagation algorithm for training a network is proposed. Examples of calculation in the Deductor Studio Lite environment are given. It is concluded that for the practical application of the model, the constant use of standard packages is inapplicable. In addition, it is also necessary to solve the problem of automating the selection of the team composition. Described is an application developed in a visual programming environment – a decision support system that allows you to import source data from an XLS file, configure input and output factors, change the architecture of the neural network (the number of hidden layers and the number of neurons in each layer), train the neural network using the backpropagation of errors, save the trained network on disk and load it again, calculate values for the input data, search for options for the composition of the team. The developed decision support system makes it possible to give recommendations on the formation of a team for a specific tournament by enumerating options.
Keywords: intellectual games, questions package, team building, forecasting, artificial neural network, perceptron, sigmoid, network training.
Decision Support System for Alarm Correlation in GSM Networks Based on Artificial Neural Networks
