The Establish Of The Mathematic Model Of Artifical Neural Network And The Realization Of BP Network For Metallogenic Prognesis

This article was completed based on the accomplishment of the algorithm design sub-theme of the National Geologic Experiment Center's 2002 project of the multiplicate geologic information processing technology based on GIS.It mainly investigated the computer programming realization of the mathematic model and computation method of the artificial Neural Network, and thus provided technical support to the nonlinear conformity management of the complex geological information.ANN( Artificial Neural Network) is a sort of large-scale self-adaptive nonlinear kinetic system, it can realize nonlinear mapping, mode identifying, function approximating, clustering analyzing, data compressing and design optimizing etc, and in the mean while, it has many favorable strong characters such as stability, astringency and robustness etc, so it has broad applications in data management area.There are lots of nonlinear problems in geological working. The integrated analyzing and sorting toward geological proved data, the accurate forecasting of the reserves and distribution of the mine resource etc all come down to the multiple data conformity disposal. Therefore, it is vitally important to develop a perfect nonlinear conformity disposal method. However, some favorable characters of ANN can content these geological working demands perfectly, so more and more people alter one after another their working focus to the nonlinear mathematic model based on the ANN in order to resolve the complex problems in geology much better.This kind of investigations have acquired some accomplishments at present, but much of these accomplishments were limited to resolve certain frondose application using certain fixed network model and were far from satisfying the multiple demands of geological proving. The purpose of this theme is to establish a multi-net-model and make it generally applicable during the arithmetic realization process to content all application demands.This article firstly made a in-depth investigation toward the network topo-structure and studying arithmetic of the three models of the ANN- BP Network, Hopfield Network and Kohonen Network, especially induct the RPROP(Flexible BP) arithmetic aiming at BP Network and made a improvement toward traditional BP arithmetic. Secondly, it discussed the process of using Visual C++ to realize these networks; Finally,a BP model for metallogenic prognosis can be constructed as the general algorithm of BP network was programmed. Users can create, design and manage BP models for metallogenic prognosis by interacting with a computer through a user-friendly interface.BP Network(Back Propagation Networks) is a kind of layer structured front-feed neural network with nonlinear mapping function. It is consisted of input layer, concealed layer and output layer, there is no connection between the nerve cells within the layers and no feedback between the layers, signals transfer along a samedirection from the input layer through the concealed layer to the output layer. Grades searching technology was adopted by the study arithmetic to minimize the public expense function and adjust the connection power by this, thus acquire knowledge and save into the connection power of the layer network. This endowed the network with the characters of preciseness knowledge structure and efficacious consequence mechanism. It was usually used to make function approaching, model sorting, data compressing etc.Hopfield Network is a kind of feedback network also called self associative memory which many favorable characters of nonlinear dynamics system. Its usual structure only has one layer of nerve cell, the feedback between each nerve cell is entire. Its study process is design a network, save a group of balance point so that when giving a group of initial values, the network finally converge to a certain saved balance point by self execute. This network is used mainly in associative memorizing, mode sorting and mode identifying etc.Kohonen Network is a kind of competitive study network, it simulate the Self-Or...