Download Artificial intelligence and robotics in manufacturing by Leondes C. (ed.) PDF

By Leondes C. (ed.)

Within the aggressive company area businesses needs to continuously attempt to create new and higher items quicker, extra successfully, and extra affordably than their rivals to achieve and continue the aggressive virtue. Computer-aided layout (CAD), computer-aided engineering (CAE), and computer-aided production (CAM) at the moment are the typical. those seven volumes supply the reader a complete remedy of the thoughts and purposes of CAD, CAE, and CAM.

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3) 6 end for The original mapping process from a plant to a neural network is shown in Fig. 2. However, this method cannot be applied to a plant that has a saturation property because the output partitioning does not affect the mapping process. For plants with a saturation property, inputs to the plant should be selected first; then the plant is determined to be saturated or not. If saturated, the saturation time is stored and used as a time window. That is, the target states are decided not from output partitioning in the original scheme but from the saturation property of the plants.

This is because the learning of the neural network is not complete. Thus, we add a Gaussian noise to the saturation time to provide a robustness against the external disturbances. 3 shows details of the data setting procedure. 3 Data-setting() // N: the number of learning patterns // // M: the number of plant input variables / // P: the number of plant output variables // for i ϭ 0 to N Ϫ 1 for j ϭ 0 to P Ϫ 1 nn_inp_pat(i, j) ← initial(j) // setting initial states // nn_inp_pat(i, j ϩ P) ← p_output(j) // setting target states // end for © 2001 by CRC Press LLC 6 for j ϭ 0 to P Ϫ 1 // adding Gaussian noise // 7 time1 ← st(j) ‫ ء‬gasdev() 8 time2 ← st(j) ‫ ء‬gasdev() 9 min_time(j) ← min(timel, time2) // determining time windows// 10 max_time(j) ← max(timel, time2) 11 end for 12 for j ϭ 0 to P Ϫ 1 // setting min.

Williams, “Dynamic Recurrent Neural Networks: Theory and Applications,” IEEE Trans. on Neural Networks, vol. 5, pp. 153–155, Mar. 1994. 21. J. J. Song, Intelligent Control of Chemical Processes Using Neural Networks and Fuzzy Systems. D. thesis, KAIST, 1993. 22. P. J. Ramadge and W. M. Wonham, “Supervisory control of a class of discrete event processes,” SIAM J. Control and Optimization, vol. 25, pp. 206–23O, Jan. 1987. 23. P. J. G. Ramadge, “Some tractable supervisory control problems for discrete-event systems modeled by buchi automata,” IEEE Trans.

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