The Application And Study Of Artificial Neural Network In Sediment Mechanic

Recent years, along with the development of economy and the endless depredation of nature, the phenomenon of water and soil loss in our country is more and more serious, which causes much more sediment into the rivers .That arouses the complexity of management to the rivers and many more severe disasters. For the sake of scientific development outlook and a harmonious society, deeper in investigation of sediment is imperatively. But, unfortunately, the problems about sediment science have not been solved completely until now.The main contents are as follows: First of all, on the basis of comprehensive review of the study achievements on sediment motion and transport, this dissertation elaborates the maximal grain size during incipient motion (MGSIM), which is the connection between sediment motion and sediment transport, moreover, this dissertation also analyses the traditional investigations of MGSIM, which are also faced with a lots of problems; at the same time, artificial neural network (ANN) is an approximate simulation of biologic nerve system, which is a network model with a special algorithm got from biologic prototype after abstractly research. This dissertation chooses the BP algorithm which is a most popular and mature artificial neural network model. The author uses VC#.NET platform, SQL2000 database system and the experiment data (the gross bed-load transport rate of Non-uniform sediment with a wide distribution in flume) to develop the forecast software, which is friendly-interface, multi-input, single output, three layers' artificial neural network base on BP and improved BP algorithms. Aiming at the classical BP algorithm's limitation, the dissertation introduces and realizes two improved BP algorithms, which are called self-adapting adjust rate algorithm and simulated annealing algorithm. Secondly, the applications of BP and improved BP algorithms in sediment science are by using the self-determination intellectual property forecast software and Matlab neural network toolbox to compute, forecast and analyze the experiment data; on the other hand, the dissertation probes into a parameter (t) of diversion function about BP algorithm. It discusses and analyses the relationship between t and computation precision of BP algorithm on the basis of abundant experiment data, furthermore it proposes a formula for values of t independently. Thirdly, the results which are computed by classical BP algorithm, self-adapting adjust rate algorithm, simulated annealing algorithm and Matlab neural network toolbox respectively compare with each other in transverse and longitudinal ways.Lastly, it draws conclusion as follows: computation in Non-uniform sediment with a wide distribution in flume experiment of stead sediment transportation by the software which is developed by VC#.NET platform satisfies with requirement of engineering, especially the simulated annealing algorithm, its stability of the network and precision of computation are superior to Matlab neural network toolbox. Those conclusions show that the applications of BP and improved BP algorithms in sediment science are feasible and also both have more values of investigation and commerce, besides, the software can be an auxiliary and referenced tool for sediment science research.