星球的光亮度
发表于 2025-3-28 18:28:24
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
发表于 2025-3-28 22:35:42
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HAWK
发表于 2025-3-29 02:58:32
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牛的细微差别
发表于 2025-3-29 05:57:51
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
发表于 2025-3-29 10:14:39
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linguistics
发表于 2025-3-29 12:41:39
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折磨
发表于 2025-3-29 15:56:49
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
发表于 2025-3-29 23:33:21
Community Education and Employabilityecurrent neural network , 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
发表于 2025-3-30 02:27:42
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
发表于 2025-3-30 06:13:36
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