Artificial intelligence in real-time control 1992 : selected by H B Verbruggen; M G Rodd; International Federation of

By H B Verbruggen; M G Rodd; International Federation of Automatic Control.; International Federation for Information Processing.; International Association for Mathematics and Computers in Simulation

The symposium had major goals, to enquire the state of the art within the software of synthetic intelligence innovations in real-time keep an eye on, and to compile regulate method experts, man made intelligence experts and end-users. many pro engineers operating in suppose that the distance among idea and perform in using keep an eye on and structures thought is widening, regardless of efforts to boost keep watch over algorithms. Papers provided on the assembly ranged from the theoretical points to the sensible purposes of man-made intelligence in real-time regulate. issues have been: the technique of man-made intelligence concepts on top of things engineering; the appliance of man-made intelligence thoughts in numerous components of keep an eye on; and and software program standards. This symposium confirmed that there exist replacement chances for regulate according to man made intelligence ideas.

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Additional info for Artificial intelligence in real-time control 1992 : selected papers from the IFAC/IFIP/IMACS symposium, Delft, the Netherlands, 16-18 June 1992

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Accurate prediction can be achieved over this range since the neural network has been trained to produce the full nonlinear model of the plant and not just a local linear approximation. A five-step-ahead predictor, represented by equation (7), was identified, based on a ( 10, 30, 1 ) Multilayer Perceptron neural network. (9) Then the system would follow this response, if g(k) was given as: g(k) = C(z-1)(r(k) -y (k)) -f(k) P (10) The control u(k) could be calculated if the inverse of this function, g"1( .

The frequency may depend on the rate of change of the model parameters. Performance of Neural Controller Figures 4 and 5 show two examples of the behaviour of the closed loop system, containing the controller and the model. e. u=O) for negative angular velocities. The system response for one normal cycle without disturbances is shown in fig. 4. At the end of the cycle the starting point has again been reached. 5 second of the forward swing and a -10° deviation from the normal starting point. The maximum angle is only 3 ° smaller than the reference angle.

05 1 0'4). gradient information a +JI aw can also be calculated by forward propagating a +J/8xi(k) instead of backpropagating a +J/aui(k). This will not be further examined here. BIT random. Several simulations were performed in which neural networks with random initial weights were trained by BIT for a range of begin Supervised Learning (SL) angles. In all these simulations the resulting The training set consisted of paired values of state networks performed badly. Stimulus u was high (input) and optimal stimulus (output).

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