Neural networks technology

Lipopolysaccharide extracts from different pathogenic and nonpatho-genic . coli strains were also analyzed by FT-IR with principle component analysis and canonical variate analysis (Kim et al, 2006b). The data showed that E. coli strains can be discriminated with >95% accuracy. Listeria species were also reliably classified by FT-IR coupled with an artificial neural network technology with a success rate of 96% (Rebuffo et al, 2006), while the identification rate for L. monocytogenes alone was 99.2%. 

Bicciato, S. Artificial neural network technologies to identify biomarkers for therapeutic intervention. Curr Opin Mol Ther 2004 6(6) 616-23. 

Spectrurr estirration using neural network technology... 

Fig. 23.5 Predicted chemical shift values using neural network technology giving 125.1 ppm for C, and 63.7 ppm for C,2, respectively.

The next major development in neural network technology arrived in 1949 with a book. The Organization of Behavior written by Donald Hebb. The book supported and further reinforced McCulloch-Pitts s theory about neurons and how they work. A major point bought forward in the book described how neural pathways are strengthened each time they were used. As we shall see, this is trae of neural networks, specifically in training a network. 

In order to describe the nonlinear stress-strain relations of the marine soft soil, a single hidden layer BP model was setup with the use of neural network technology. For the model, the input values are bias stress, confining pressure and time, the output value is the strain. Therefore, nodes of the input layer is 3, the number of nodes of the output layer is 1. The number of hidden layer units ranging from 5 to 25, and it need to be determined based on the training and fitting results. The neurons in the hidden layer is a sigmoid transform function, the neurons of the... 

M. Landin, and R. Rowe, Artificial neural networks technology to model, understand, and optimize drug formulations, in Formulation Tools for Pharmaceutical Development, 7-37,... 

Experimental method is explained with the application of neural networks technology, the relationship with the environment, the type of robot, and how it interacts. It warns the different behaviors of the robot. When it starts from different points along the eourse of training or when it does start from the same point but with different orientation for the same course of training. 

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