Application of Wavelet Neural Network in Cortisol Solubility

Abstract:

Wavelet neural network(WNN) was applied to predicate the cortisol solubility. The model consists of a multilayer feedforward hierarchical structure, and the flow of information is directed from the input to the output layer by using wavelet transforms to achieve faster convergence. By adaptively adjusting the number of training data involved during training, an adaptive robust learning algorithm is derived for improvement of the efficiency of the network. The neural network was trained and simulated cortisol solubility with different input and output parameters. Simulation results confirmed that this approach gave more accurate predictions solubility.

• Periodical: Advanced Materials Research (Volumes 396-398)
• Pages: 711-715
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.396-398.711
• Online since: November 2011
• Authors: Jian Xin Chen, Xu Na Shi, Shu Chun Pang, Mei Jing Zhang, Sheng Yu Li
• Keywords: Cortisol, Prediction, Solubility, Wavelet Neural Network (WNN)

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