Study On Subnet Dynamic Integration Method Of Modular Neural Networks And Development Of Simulation System

A Modular Neural Network (MNN) is a Neural Network (NN) that consists of several modules, each module carrying out one sub-task of the neural network's global task. Networks are combined in a fashion of competition and cooperation to improve the performance of the overall system. A great deal of successful applications demonstrates that modular neural network outperforms single neural network in terms of generalization and reliability. And modular neural network can be effectively used to solve the single neural network's problem in its application and implementation for us with a new tool for problem-solving. Having recognized that modular neural network has an enormous potential and bright prospect in application, a large number of researchers plunge them into the field, yielding a lot of relevant theories and application achievements. Also it has become a hot topic in both machine learning and neural network fields.In this dissertation, the first aspect is mainly studied in the context of regression problems. Three methods for integrating the component networks base on thinking of "Knowing something of everything and everything of something" are presented. These methods take full advantage of the training samples, and educate a series of training units by use of moderation zoom training pack. Not only are these neural network units in possession of corresponding veracity, but also have the flexibility to other samples. To make a tactics collection for the unit in this kind of network will improve the speed of getting closer, the ability of anti-jamming, flexibility and make a great progress in the system. This dissertation make an emulation test by using 8 regression problems instances, and the results reflect the capability in other sides analyzed by 4 evaluation standard. Compared the result with other results, the method "Knowing something of everything and everything of something" wins the best in most of experiments. No matter what degree of difficulty the problems are, this method could retain good flexibility and error requirements. For complicated problems, the characteristic of precision advantage could be represented obviously.This dissertation also presents the Simulation System of Modular Neural Network. The system, designed by author, is propitious to the research of modular neural network, it describes the design in total for system implementation, each module's functions including input/output, algorithms, interface, program logic, memory distribution, and the design of system interface, etc. In order to get somebreakthroughs and innovation on the base of the original algorithm, this system makes progress on some sections and builds up a platform for the experiment and research in modular neural network. Because each module has opening, general, and extensibility characteristics, in upper period, the system could provide advantaged support for the practice teaching and science exploring.