الفهرس 
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Abstract
The modernized mr defence system should include anti aircraft artillery because of the efficient jamming, interference, E.MP (Electro magnetic pulse), and intelligent counter measures used extensively against air defence means. Under the condition of one or more ofthe above weapons applications, the command controlled missile guidance stations may have reduced efficiency or have completely disabled function integrity. In this case the anti- aircraft artillery contributes well to the system capability.
In this thesis we presents a system using an enb’y computer audio visible (E.CA V) unit with an Artificial Neural Network (A.N.N.) to control the aiming of the anti aircraft gun by the human voice, and identify and classifY the kinds ofthe targets relative to its echoes and images. The system is suitable for controlling the 23¬mm gun used in air defence force, the hawen gun-using periscope, and for rocket cranes. It can be also llsed in the ”home ofthe future”, etc .... The proposed system input data is fed ITom sound transducers through an audio channel and images profile through a video camera.
The used algorithm for the N.N. training is the feed-forward backpropagation with momentum term. The Neural Network receives the input data as (up-down-right¬left) commands of the operator, and then it recognizes the user sound print, so that a specific person controls sWlomechanism to aim it to the required position. Tbe N.N. also receives the echoes of the targets and its images to classifY the kinds of the moving targets and then the corresponding available parameters of the targets and options are viewed on the output display panel.