Aiming at the drawback in classical BP artificial networks and combining with genetic algorithms, this paper puts forwards the tax forecasting model based on real number coded GA-BP artificial networks;

The detection, location, and estimation problem of structural damage has been the important subject of structure engineering research in recent years. The damage identification methods have been widely used in aeronautical, civil, mechanical and nuclear structures. It is polytechnic method constructed on the damage theory, sensor technology, signal analysis, computer science and artificial networks.

An Artificial Networks Based on Uniform Design and Its Application

基于均匀设计法的人工神经网络及其应用(英文)

The detection, location, and estimation problem of structural damage has been the subject of much current research in recent years. The damage identification methods have been widely used in aeronautical, civil, mechanical and nuclear structures. It is polytechnic method constructed on the damage theory, sensor technology, signal analysis, computer science and artificial networks.

Scale-free graphs and their properties have attracted considerable interest over the last 4-5 years, after it was discovered that many natural and artificial networks belong to this category.

Some of these assumptions are rather hypothethical and debatable for the time being and they will become justified in the future only if there will be more progress in the empirical and theoretical research on the brain and on artificial networks.

Artificial networks, including - random, regular 4-fold and 5-fold lattice and a tree-like structure; 2.

The study confirms the potential of artificial networks for the classification of bioprosthetic valve closure sounds.

We tested several measures for identifying vulnerable edges and compared their prediction performance in biological and artificial networks.

It is suggested to study the neural network in the view of neural functions. The memory function of network is studied and its mathematical model is given. The model is synthesized by a peicewise-linear resistive network. The network is of many properties of artificial network such as parallelism, real-time processing, distributivity and adaptability etc., in addition, the parameters of network are expressed analytically by the patterns and features which are memorized in...

It is suggested to study the neural network in the view of neural functions. The memory function of network is studied and its mathematical model is given. The model is synthesized by a peicewise-linear resistive network. The network is of many properties of artificial network such as parallelism, real-time processing, distributivity and adaptability etc., in addition, the parameters of network are expressed analytically by the patterns and features which are memorized in the network.

The layer recognition in the process of integrated logging interpretation can be regarded as a classification problem based on pattern recognition. It's known to us that artificial neural networks work very well in operating classification and learning and it's tolerant of errors as well. This paper will first introduce the current situation of studying artificial networks with the emphasis on the fundamentals of the back-propagation network. A method of input swapping is then presented to deal with...

The layer recognition in the process of integrated logging interpretation can be regarded as a classification problem based on pattern recognition. It's known to us that artificial neural networks work very well in operating classification and learning and it's tolerant of errors as well. This paper will first introduce the current situation of studying artificial networks with the emphasis on the fundamentals of the back-propagation network. A method of input swapping is then presented to deal with the problem of network covergence. Some results from the experiment on applying the artificial neural network method to layer recognition are given at the end of the paper.

This paper had reviewed the progress of artificial network. The some basic structures of the network and the training procedures were related. Finally, the present state and prospects of the applications of neural network techniques in the process control field were discussed.