Research On Neural Networks And Distributed Computing Based On Multidimensional Data Analysis

Artificial neural network technology is a topic concerned by scientists in many domains, because of its characteristics such as massive parallel process, distributed storage, self-adaptability, fault-tolerant and so on. It has been widely applied in many fields such as biology, electronics, computer science, mathematics and so on. With the rapid development of network communication technology and Internet, the distributed computing has become one of the key technologies influencing today's development in computer technology. And it has been used in modern society and economic development. Both of the technologies need data, however, lots of data come from the multidimensional data stored in data warehouse. Both of the technologies need data analysis, which will involve multidimensional matrix. Therefore, it has important meaning to study the artificial neural networks and distributed computing based on multidimensional data analysis, so our research was supported by National Natural Science Fund of China.This dissertation is divided into four parts as follows.The first part focuses on the study of multidimensional data analysis and multidimensional matrix. We introduce the concept of multidimensional matrix, according to necessity of using multidimensional data analysis in data warehouse. Then we discuss the properties of cubic matrix which has the most widely application in multidimensional matrix, so we establish basis for application in neural network and distributed computing.The second part focuses on the study of artificial neural networks based on multidimensional data analysis. At first, we proposes a kind of unsupervised learning neural network model with convex constraint which has special structure and can realize the compression of data and reduction process. The main characteristics of the neural network can represent information after being trained. Secondly, we study a kind of Bayes neural networks, and adopt general naive Bayes to handle continuous variables, then, propose a kind of kernel function constructed by orthogonal polynomials which is used to estimate the density function of prior distribution in Bayes network, furthermore, make researches into optimality of the kernel estimation of density and derivatives. Thirdly, aiming at research of total factor productivity (TFP), we construct a fork neural network to implement TFP measure by stochastic frontier model. Finally, in order to compute TFP contribution rate, we put forward a kind of semi-supervised heterogeneous neural networks which makes output results consistent by interaction. Also we discuss the construction and algorithm of this neural network in detail.The third part concerns distributed computing based on multidimensional data analysis. Firstly, we propose an improved partial least square algorithm in structural equation model (SEM), which constructs a deterministic algorithm. Then multi-group structural equation model is analyzed and distributed computing is adopted to calculate all the coefficients. Furthermore, a uniform model is built using the generalized linear model with convex constraint and an algorithm for the multi-group SEM is presented. Moreover, we put forward the multivariate nonparametric regression curve drift model, and apply distributed computing to forecast the sale curve of multivariate curve drift model. At last, we apply distributed computing to several fields, which include Monte Carlo distributed computing for general distribution function table of probability of statistics, distributed computing for modeling the decomposition products of a protein and bootstrap analysis of MOSFET life distribution with negative order moment estimate and its distributed computing.The final part is an integrated application of neural networks and distributed computing based multidimensional data analysis. This dissertation introduces customer satisfaction index measure analysis system which is a large application system developed by our team. The system is based on data warehouse and .NET technique, uses the structure of unsupervised learning neural network model with convex constraint, and realizes network remote calculation and distributed computing.