Approach for Load State Detection in MIN

Some thoroughly research on the basic method for load state detection in MIN is introduced. Load sources of SCP and SSP in MIN are analyzed in detail after investigating the relationship between inter resource and load state in the system. And load detection parameters and model are presented. Moreover, a novel strategy to detect system load state based on fusion of multi detection data is presented. By some test, the theory and method are proved most feasible. Our research show that load state of MIN can be recognized by detecting utility of internal resource utilizing of single node in MIN, and multi redundant detecting data fusion is effective approach to improve the reliability and trust degree of the result.     

Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System

While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.     

Study of IP over WDM System Structure

WDM technology is the basis of optical network implementation. Due to eliminating SONET/SDH and ATM elements, and allowing direct connection with IP routers, IP over WDM can reduce network cost and simplify network expansion . In this thesis, the structure and protocol layers of IP over WDM are firstly explained. Then the procedures of this network are described briefly. At last, the problems and future of IP over WDM are analyzed     

The Software Implementation of Neural Network Fuzzy Inference Cooperation System

This poper sets up a new Neural Network Fuzzy inference Cooperation system which is based on fuzzy inference principle in Expert Systems,take advantage of the powerful learning function, the little sensitivity to input layer's samples,and the fast convergence and so on in FCBP and take advantage of the good sort property and good suit property to fuzzy value in MFART Network.     

The Inherent Fault Tolerance of Multi-Layer Neural Networks

The inherent fault tolerance of multilayer neural networks (MLNs) is due to thedistributed properties of information processing and information storage in an MLN , and the inher-ent fault-masking capability of every neuron. Also,the concept of inherent fault tolerance of MLNsis formalized, Finally, perceptrons and three-layer nets are shown inherently fault tolerant underreasonable statistical conditions,respectively.     

Automatic Fingerprint Classification by GA-Based Neural Network

Fingerprint classification can provide an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for large database. In the paper, by combining genetic algorithm and neural network is presented a fingerprint classification algorithm which is able to achieve an accurate classification. By inputting the global feature represented by directional image to three layer neural network trained by genetic algorithm, the fingerprints were classified into six categories: whorl, right loop, left loop, arch, double loop and undiscerning type successfully.     

A Muti-Objective Network Planning Model for Project

Based on the network planning model developed by the author for an overhaul project of a rolling mill production line in a heavy steel plant, an new multiobjective network planning model is proposed in this paper. The formulations of network time parameters are derived. The optimization problem of multiobjective network planning is also discussed. And it has been found out that the fractional single-objctive network is only one of the cases in this model.