Titlebook: Advances in Neural Networks - ISNN 2007; 4th International Sy Derong Liu,Shumin Fei,Changyin Sun Conference proceedings 2007 Springer-Verla

星球的光亮度

Robert Mier,Joan Fitzgerald,Lewis Randolph using static displacements and frequencies constitutes the input parameter vectors for neural networks. A damage numerical verification study on a five-bay truss was carried out by using an improved momentum BP neural network. Identification results indicate that the neural networks have excellent

昏睡中

HAWK

牛的细微差别

David Fasenfest (Assistant Professor)ocal minimal. Genetic algorithm (GA) is a parallel and global search technique that searches multiple points, so it is more likely to obtain a global solution. In this regard, a new algorithm of combining GA and NN is proposed here. The GA is employed to exploit the initial weights and the NN is to

极肥胖

linguistics

折磨

Community Education and Employabilitye of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker, we divide S

公式

Community Education and Employabilityecurrent neural network [14], this paper presents a recurrent neural network for solving variational inequalities with general linear constraints in real time. The proposed neural network has one-layer projection structure and is amenable to parallel implementation. As a special case, the proposed n

讥讽

Encountering Social and Community Education,network used in this scheme is the Complex Bilinear Recurrent Neural Network (CBLRNN). A reduced version of the CBLRNN for faster and stable convergence is first proposed in this paper. The R-CBLRNN is then applied to equalization of a wireless ATM channel for 8PSK, which has severe nonlinearity and

启发

Community Eldercare Ecology in ChinaNN is based on the bilinear polynomial, it has been successfully used in modeling highly nonlinear systems with time-series characteristics and the BLRNN can be a natural choice in predicting electric load. The performance of the proposed BLRNN-based predictor is evaluated and compared with the conv