By John G. Webster (Editor)
Read or Download 39.Neural Networks PDF
Best hydrology books
Complete account, treating either theoretical and utilized elements of particles circulation. The textual content starts with a dialogue of primary mechanical points, reminiscent of circulation features, sort class, mechanics, incidence and improvement, fully-developed circulation and deposition techniques. the second one a part of the booklet sheds mild at the software of thought on the subject of computer-simulated reproductions of actual failures.
This booklet makes a speciality of how hydrogeophysical tools may be utilized to unravel difficulties dealing with environmental engineers, geophysicists, agronomists, hydrologists, soil scientists and hydrogeologists. We current functions of hydrogeophysical ways to the knowledge of hydrological tactics and environmental difficulties facing the move of water and the delivery of solutes and contaminants.
The curve quantity (CN) strategy for estimating direct runoff reaction from rainstorms was once built to fill a technological area of interest within the Nineteen Fifties. because then, use of the CN strategy has prolonged to different purposes, and person event and research have redefined a number of positive factors of the unique know-how.
- Groundwater Engineering
- Advances in Geosciences: Volume 11: Hydrological Science (Hs)
- Economics of Water Resources: From Regulation to Privatization
- Groundwater Contamination and Emergency Response Guide (Pollution Technology Review)
- Water transmission and distribution : student workbook
- Trends in Continuum Mechanics of Porous Media
Additional info for 39.Neural Networks
Gislen, C. Peterson, and B. , 4: 805–831, 1992. 6. R. S. S. thesis, Florida Institute of Technology, 1992. 7. D. H. Ackley, G. E. Hinton, and T. J. , 9: 147–169, 1985. 8. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by simulated annealing, Science, 220 (4598): 671–680, 1983. 9. S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. , PAMI-6: 721–741, 1984. 10. C. Peterson and J. R. , 1: 995–1019, 1987.
Dm(k)]T is an unknown disturbance with known upper bound so that ʈdʈ Ͻ bd, x(k) ϭ [x1(k), x2(k), . , xn(k)]T ʦ Rn, and f ϭ [f 1, f 2, . , f m]T : Rn Ǟ Rm is a smooth vector function. Output Tracking Problem. Given the system in Eqs. (17) and (18), it is required to manufacture a bounded control input u(k) ϭ [u1(k), u2(k), . , um(k)]T such that the output y(k) of the system tracks a specified desired output yd(k) ϭ [yd1(k), yd2(k), . , ydm(k)]T while ensuring that the states x(k) are bounded. It is assumed that the desired output satisfies yd (k) y (k + 1) d ≤ γ, k = 0, 1, 2, .
Rigorous research in NN for closed-loop control is being pursued by several research groups. Narendra et al. (5,6,9) 153 emphasizes the finding of gradients needed for backprop tuning. Sadegh (11) employs approximate calculations of the gradient to establish stability results, and Cristodoulou (12), Ioannou (13), Sadegh (11), and Slotine (14) offer rigorous proofs of performance in terms of tracking error stability and bounded NN weights. All these works assume that the NN is linear in the unknown parameters by employing single-layer NNs or recursive NNs with special structures.