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

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

ISBN-10: 0080418988

ISBN-13: 9780080418988

The symposium had major goals, to enquire the state of the art within the program of synthetic intelligence suggestions in real-time regulate, and to assemble keep watch over approach experts, man made intelligence experts and end-users. many pro engineers operating in suppose that the distance among conception and perform in using keep watch over and platforms concept is widening, regardless of efforts to boost keep an eye on algorithms. Papers offered on the assembly ranged from the theoretical features to the sensible purposes of man-made intelligence in real-time keep an eye on. issues have been: the method of man-made intelligence concepts up to the mark engineering; the applying of synthetic intelligence thoughts in several components of regulate; and and software program necessities. This symposium confirmed that there exist substitute probabilities for keep an eye on according to man made intelligence thoughts.

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Additional resources 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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Vector and the next when applied to the BFGS optimisation algorithm. A nonlinear series-parallel model was then trained, using the parallel BFGS algorithm on 10 T800 transputers and a training set of 500 vectors. The network used was a (6, 15, 1) Multilayer Perceptron, with the nonlinear elements in the hidden layer being tanh(x/2) sigmoid functions. 4 seconds. The response of the series model after one hundred iterations through the training set, taking approximately one minute, is as shown in Fig.

1 ) is minimized. A characteristic feature of NN models is that the where dimension of 0 is quite high, often several hundreds. From an estimation point of view this should raise (Yk , Xk ), (2 . 2) some worries, since it is bound to give a large "vari­ (Z1 , Z2 , . . ZN]r. 3) ance error" , i. e. , the modeling error that origi­ nates from the noise disturbances in the estimaFigure 1 shows a feed forward NN with one hid­ tion ( "training" ) data set causes misfits when the den layer. In the first layer, the input layer, no model is applied to a validation ( "generalization" ) computation is done.

The controller adapted relatively fast to a changing model parameter. Still for our purposes a faster adaptation is desired_ Therefore implementation of the weight adaptation process on transputers is being evaluated. The controller has not as yet been tested experimentally on a human leg. Network Adaptation to Changing Model Parameter BTT after SL resulted in a better controller, at the cost of a longer training phase. Another advantage of the BTT algorithm is its ability to compensate for changing model parameters directly_ This is possible, since a model is used each iteration for the calculation of the gradients a +J/8w.

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


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