裸子植物体细胞胚胎发生和人工种子的研究

该文介绍了一种作为森林种子的大规模增值的有效方法－－裸子植物体细胞胚胎发生和人工种子的研制。其内容包括：（１）胚性愈伤组织的诱导与保存；（２）影响体胚发生的因素；（３）体胚的发育与成熟；（４）大规模培养体胚；（５）成熟体胚的干化、包理与人工种子生产；（６）体胚的萌发和转换。最后对人工种子的应用前景进行了讨论。     

Expert System for Artificial Intelligence Blast Furnace Smelting Process

In order to upgrade traditional industry and realize the automation of furnace condition judgement and standardization of operation, an expert system for various furnace conditions and breakdown judgement and operation guidance on blast furnace are developed based on the theory of artificial intelligence and knowledge engineering. The system is fulfilled through C++Builder and combined with the actual production picture. The system has efficient and convenient man-machine interface by using menu operation method. The testing and operation of actual production data in simulation system indicate that the system is exact and reliable and can provide both the warning of various furnace breakdown and corresponding operation guidance.     

Artificial Neural Networks Models for Flow Stress during High Temperature Plastic Deformation of Al-Li Alloy

Using gained experimental data to develop the models of stable flow stresses at high temperature plastic deformation by statistical methods for alloy materials, precision of the models is poor and at the same time the processes of modeling are complicated with great workload. On the basis of the data obtained on Gleeble-1500 Thermal Simulator,the predicting models for the relation between stable flow stress during high temperature plastic deformation and deformation strain, strain rate and temperature for 1420 Al-Li alloy have been developed with BP Artificial Neural Network method. The results show that the model on basis of BPNN is practical and it reflects the real feature of the deforming process. It states that the difference between the real value and the output of the model is in order of 5 percent.     

An Expert System Approach to the Computer Aided Design of Intelligent Controllers

The idea for an expert system approach to the computer aided design of intelligent controllers (ESCAD1C) is discussed and the idea of two step modelling of an intelligent controller is presented on the basis of emulating human control behaviours to solve complex problems. The structure, functions and characteristics of ESCADTC system , and the design procedures of intelligent controllers are gived. The knowledge base and simulating functions for designing intelligent controllers are also described.     

Multiple Layer BP Neural Network for Fuzzy Calculating

The FCBP(Fuzzy calculating BP) algorithm which is proposed by this paper hasovercome the sensitivity for samples,reduced the number of input layer's samples,lightened the burden of input layer.it is suitable to fuzzy inference and pattern recognition.     

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.     

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.