An Artificial Neural Network (ANN) is defined as a computational model that imitates the functioning of biological neurons. It consists of input, hidden, and output layers, processing molecular information to produce biological activity or properties as output. ...
In feed-forward neural network, when the input is given to the network before going to the next process, it guesses the output by judging the input value. After guess, it checks the guessing value to the desired output value. The difference between the guessing value and the desired output ...
Other modifications are envisioned in the future. For example, not all potential parameters are equally sensitive to local environments. To improve the computational efficiency, the parameters can be divided into two subsets40: local parametersai = (ai1, ...,aiλ) adjustable according to the...
Calculation method of ship collision force on bridge using artificial neural network. Journal of Zhejiang University-SCIENCE A , 9 (5):614–623. [:10.1631/jzus.A071556]W. Fan, W. C Yuan and Q. W. Fan, "Calculation Method of Ship Collision Force on Bridge Using Artificial Neural Network"...
On the other hand, an artificial neural network is used to construct the prediction model of influencing factors for green development behavior adopted by construction enterprises, which is a new application of this method into the field of the construction industry. In terms of practical ...
Calculation method of ship collision force on bridge using artificial neural net- work. Journal of Zhejiang University-SCIENCE A, 9(5):614-623. [doi:10.1631/jzus.A071556]Wei FAN,Wan-cheng YUAN,Qi-wu FAN. Calculation method of ship collision force on bridge using artificial neural network[J]...
It is worth point out that back propagation is the most well-known and widely used error calculation method for neural network, however, one of its main drawbacks is the computational complexity particularly in a huge network. Network topology refers to the arrangement or structure of the ...
fig 1 BParti icialneuralnetwork orpredicting thixotropyo waxycrudeoilS 3 R-G 模型与人工神经网络模型学 习和预测原油触变应力的结果及 其分析 从表 1 中选出 组实验数据 ( 对应于 7 = 11. ~46.8~9 .6S 1 共 54 个样本 ) 为学习样本 集 并对网络进行训练 训练结果列于表 Z 中 ; 对 表 1 中...
a response variable in a neural network is a function of all hidden variables. An advantage of multiple-cause models is that they are representationally efficient. If each hidden variable has a Bernoulli distribution, for example, and if one seeksNbits of information about the underlying state of...
A combination of keywords and MeSH terms was used to identify RCTs on interventions involving AI, for example: “artificial intelligence”, “decision support system”, “deep learning” and “neural network”, in addition to specific terms such as “naïve bayes”, “random forest” and “mu...