XPost: comp.ai.genetic, comp.ai.neural-nets, comp.robotics.misc   
      
   Say we have a vehicle (a car or a boat or a helicopter or an airplane etc)   
   which reacts to control inputs from a driver/pilots in a particular way   
   defined by its current state and control input.   
      
   Assume that we have a car travelling on flat road. Our control input would   
   be a vector of   
      Ctrl_Input = [Steering Angle, Gear, Accelerator Value, Brake Value]   
   and current state would be a vector of   
      Current_State =  [Velocity X, Velocity Y, Position X, Position Y,   
   Acceleration X, Acceleration Y]   
   the next state of the vehicle after a small time increment "dt" would be   
      Next_State =  [Velocity X, Velocity Y, Position X, Position Y,   
   Acceleration X, Acceleration Y]   
      
   In another word we have a system with following input and output   
      
       Inputs:   
         Ctrl_Inputs   
         Current_State   
      Output: (obtained after "dt" time)   
          Next_State   
      
   Assume that we have as a set of Ctrl_Inputs, Current_State and Next_State   
   data obtained in an experiment inwhich a human driver driving the vehicle on   
   a flat  road.   
      
   Given these data sets we should be able to train a neural network so that it   
   relate inputs to outputs.   
   My questions are as follows;   
      1- Which neural network topology would be good for learning vehicle   
   behaviour (dynamic model of the vehicle) from above mentioned set of data ?   
       2- Number of neurons and number of layers required?   
       3- If we know our current state and our desired state (next state) how   
   we can use the neural network to find required Control_Input vector?   
       4- Is there any tutorial/demo/code of this kind ?   
       5- Thanks.   
      
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