Twenty-Fourth Symposium on Naval Hydrodynamics (2003) / Chapter Skim
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Using Recursive Neural Networks for Blind Predictions of Submarine Maneuvers
Using Recursive Neural Networks for Blind Predictions of Submarine Maneuvers
Pages 721-743
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From page 721... ...
Specifically, all participants were asked to provide trajectory, attitude, velocity and acceleration predictions for a set of 34 maneuvers for which only the initial conditions and the time histories of control surface positions and propeller rotation speed were made available. Both data sets included constant heading runs, vertical and horizontal overshoots and controlled and fixed plane turns (many with combined deflection of multiple appendages)
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From page 722... ...
The input data consist of the initial conditions of the vehicle and time histories of the control variables: propeller rotation speed and rudder and sternplane deflection angles. As the simulation proceeds, these inputs are combined with past predicted values of the state variables (outputs)
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From page 723... ...
are aligned with the centerline of the axisymmetric hull. The propeller used to drive the ONR Body 1 RCM is a modification of a commercially available 3bladed right-handed motorboat propeller.
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From page 724... ...
The maneuvers included constant heading runs, vertical and horizontal overshoots and turns. Seventy-eight such data files spanning all maneuver types were provided to each participant.
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From page 725... ...
A recursive neural network has feedback; the output vector is used as additional inputs to the network at the next time step. For the first time step, when no outputs are available, these inputs are filled with initial conditions.
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From page 726... ...
To T: 11 _ 6 6 80 terms were fashioned from the basic control variables: propeller rotation speed, n; sternplane and rudder deflection angles, As and Or. Also available for the definition of the input terms are output variables from the previous time step, which are recursed and made accessible to the input side of the network.
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From page 727... ...
The latter quantity is calculated from ax = fir - tang ( v - r L; ) ~7' When a transverse velocity component, v, or a yaw angular velocity, r, is present, the simple result that angle of attack equals rudder angle must be corrected.
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From page 728... ...
Data files containing time histories of all of the variables described in Table 1 for such maneuvers as constant heading runs, vertical and horizontal overshoots and turns formed the training sets. After the neural network has been successfully trained, the weights are no longer modified and remain fixed.
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From page 729... ...
The absolute error was 0.161, the average angle measure was 0.964 and the correlation coefficient was 0.945. At this epoch the average angle measure reached its maximum, and the absolute error and the correlation coefficient were very close to their minimum and maximum, respectively.
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From page 730... ...
The arbitrator graded only the blind maneuvers obtained from each participant, and used a grading system that relied on the Average Angle Measure. The Average Angle Measure was developed by the Maneuvering Certification Action Team at NSWCCD in 1993-1994 (see July and August 1994 reports)
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From page 731... ...
Entrance Angle= 10°, Speed= 1.8 m/s Predictions: solid black lines, Measured: dashed red lines.
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Entrance Angle = 15°, Speed = 3.0 m/s Predictions: solid black lines, Measured: dashed red lines.
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From page 733... ...
Entrance Angle = 35°, Speed= 1.8 m/s Predictions: solid black lines, Measured: dashed red lines.
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From page 734... ...
14 Overall Challenge results by maneuver type. The figure plots an average angle measure averaged over all of the runs of a particular maneuver type for each of the participants in the Challenge.
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From page 735... ...
CONCLUSIONS A recursive neural network maneuvering simulation, trained on constant heading runs, vertical and horizontal overshoots and controlled and fixed plane turns, was used to produce predictions for 78 known runs and 34 blind maneuvers of a radiocontrolled submarine. An independent arbitrator graded the 34 blind runs and assigned 32 grades of Good and 2 grades of Fair, and this was the highest total score obtained by any participant for the blind predictions.
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From page 736... ...
NAVY Surface Ships," David Taylor Research Center Ship Hydromechanics Department Research and Development Report DTRC/SHD-1320-01, April 1989, pp.
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From page 737... ...
A series of neural networks could be trained such that each was used to predict just one maneuver type. The trained networks could then be combined with appropriate switching logic into one simulation.
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From page 738... ...
It has been this discussor's experience that viscous effects account for 90 percent of the unpredicted submarine maneuvering behavior observed. Adverse viscous effects include upstream generated vortices landing on control surfaces, interactions between appendages and body shed vortices, separation on control surfaces and body, and appendage/turbulent boundary layer interaction.
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From page 739... ...
The authors have work under way in which a recursive neural network can be used not only as a predictive tool but also as a design tool. This next generation code forms a partnership with grid generation tools and RANS codes to effectively incorporate geometry information and to construct a force and moment database to be used as training data for the neural network.
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From page 740... ...
However, the uncertainty in the weights does not grow without bound as training proceeds because the backpropagation algorithm continually corrects the weight set to minimize error. The degree to which the training data may be reproduced by the trained network will depend upon how effectively the backpropagation algorithm can refine the weight set.
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From page 741... ...
1. The set of partial derivatives that are required are ' and will be derived step by step using repeated application of the OKlnput Chain Rule.
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From page 742... ...
(9) Application of the chain rule for the case of multiple independent variables requires a .
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From page 743... ...
( 12) OUl~putj For a specified input vector and weight set, a computer subroutine implementing the feedforward equations (Eqs.
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