Abstract: A control device of a vehicle comprises a vehicle control part 61 configured to use a probability distribution of at least one predetermined parameter to calculate an expected value of each of at least one evaluation value and control the vehicle 1 based on the expected value.

Abstract: A control device of a vehicle comprises a vehicle control part 61 configured to use a probability distribution of at least one predetermined parameter to calculate an expected value of each of at least one evaluation value and control the vehicle 1 based on the expected value.

Abstract: An engine is provided with an ECU for executing engine control by using various control parameters. The ECU includes a learning map storing a learning value of the control parameter and executes weighting learning control of the learning value. In the weighting learning control, each time the control parameter is acquired, a weight wkij decreasing larger if a distance from a position of an acquired value zk of the control parameter to a grid point is larger is set to each of the grid points of the learning map. Then, on the basis of the acquired value zk of the control parameter and the weight wkij, the learning values Zij(k) at all the grid points are updated. As a result, all the learning values can be efficiently updated in one session of the learning operation.

Abstract: This invention has an object to enable update of learning values of a large number of grid points in one session of a learning operation and also to easily adjust learning speed and efficiency in a wide learning region. An engine 10 is provided with an ECU 60 for executing engine control by using various control parameters. The ECU 60 includes a learning map storing a learning value of the control parameter and executes weighting learning control of the learning value. In the weighting learning control, each time the control parameter is acquired, a weight wkij decreasing larger if a distance from a position of an acquired value zk of the control parameter to a grid point is larger is set to each of the grid points of the learning map. Then, on the basis of the acquired value zk of the control parameter and the weight wkij, the learning values Zij(k) at all the grid points are updated. As a result, all the learning values can be efficiently updated in one session of the learning operation.