Abstract: An apparatus and method for controlling trajectory of an object (47) to a first predetermined position. The apparatus has an input layer (22) having nodes (22a-22f) for receiving input data indicative of the first predetermined position. First weighted connections (28) are connected to the nodes of the input layer (22). Each of the first weighted connections (28) have a coefficient for weighting the input data. An output layer (26) having nodes (26a-26e) connected to the first weighted connections (28) determines trajectory data based upon the first weighted input data. The trajectory of the object is controlled based upon the determined trajectory data.

Abstract: An apparatus and method for controlling trajectory of an object (47) to a first predetermined position. The apparatus has an input layer (22) having nodes (22a-22f) for receiving input data indicative of the first predetermined position. First weighted connections (28) are connected to the nodes of the input layer (22). Each of the first weighted connections (28) have a coefficient for weighting the input data. An output layer (26) having nodes (26a-26e) connected to the first weighted connections (28) determines trajectory data based upon the first weighted input data. The trajectory of the object is controlled based upon the determined trajectory data.

Abstract: An apparatus and method for controlling trajectory of an object (47) to a first predetermined position. The apparatus has an input layer (22) having nodes (22a-22f) for receiving input data indicative of the first predetermined position. First weighted connections (28) are connected to the nodes of the input layer (22). Each of the first weighted connections (28) have a coefficient for weighting the input data. An output layer (26) having nodes (26a-26e) connected to the first weighted connections (28) determines trajectory data based upon the first weighted input data. The trajectory of the object is controlled based upon the determined trajectory data.

Abstract: A neural network controller for a pulsed rocket motor tactical missile. The missile includes a fuselage or body, with a propulsion system. The pulsed propulsion system has a need for a logical control of the application of propulsion energy throughout the missile's flight. The controller is trained to provide optimal initiation of individual rocket motor thrust pulses based on tactical information available at various points/times in the missile's flight. The controller training is through use of training cases, in which the network learns to output a specific target value(s) when specific values are input. When trained with a large sample of training cases selected from the multidimensional population of interest, the neural network effectively learns the correlations between inputs and outputs and can predict input/output relationships not previously seen in any training case.