Characterization of gas pipeline inspection signals using wavelet basis function neural networks

An Enhanced Wavelet Neural Network Model with Metaheuristic Harmony Search Algorithm for Epileptic Seizure Prediction

“…A set of good translation vectors will ensure a better learning process, as well as faster convergence for the neural network model under consideration. From the literature, several approaches have been used to initialize the translation vectors, which include random points from the interval of the domain used [7], dyadic selection scheme with k-means clustering algorithm [19], and a novel fuzzy c-means clustering algorithm, named modified point symmetry-based fuzzy c-means algorithm [8].…”

Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems

“…A Euclidean distance-based connectionist clustering method was presented by Lin [12], in which the cluster means of the input data were positioned as the translation vectors. Hwang et al [13] employed the K-means clustering algorithm to find the optimal locations of the translation vectors. The obtained solutions were updated subsequently by using a dyadic selection scheme in order to eliminate unnecessary hidden nodes.…”

The Gas Transportation in a Pipeline Network

“…The authors however noticed that there is necessity of verification of work effects with real data. Based on the results from magnetic flux leakage signals, Hwang et al (2000) presented a new approach for training, hierarchical wavelet basis function neural network for the three-dimensional characterization of defects on the pipeline. Nguyen et al (2006Nguyen et al ( , 2008 use the Neural Network for forecasting the demand of the hourly gas stream for customer.…”