Search

Generalized Regression Neural Network (GRNN), is a radial basis function based supervised learning type Artificial Neural Network (ANN) which is commonly used for data predictions. In addition to its easy modelling structure, being fast and producing accurate results are the other strong features of it. On the other hand, GRNN employs a neuron in pattern layer for each data sample in training data set. Therefore, for huge data sets pattern layer size increases proportional to the number of samples in training data set, memory requirement and computational time also increase excessively. In this study, in order to reduce space and time complexity of GRNN, k-means clustering algorithm which had been used as pre-processor in the literature is utilized and outlier data emergence which affects the performances of previous studies negatively, is prevented by identifying test data located between clusters. Hence, while memory requirement in pattern layer and number of calculations are reduced, negative effect on the performance emerged by the use of clustering algorithm is significantly removed and almost the same prediction performances to that of standard GRNN are achieved by using 90% less training samples. [ABSTRACT FROM AUTHOR]

Abstract: Complex variability is a significant problem in predicting construction crew productivity. Neural Networks using supervised learning methods like Feed Forward Back Propagation (FFBP) and General Regression Neural Networks (GRNN) have proved to be more advantageous than statistical methods like multiple regression, considering factors like the modelling ease and the prediction accuracy. Neural Networks using unsupervised learning like Self Organizing Maps (SOM) have additionally been introduced as methods that overcome some of the weaknesses of supervised learning methods through their clustering ability. The objective of this article is thus to compare the performances of FFBP, GRNN and SOM in predicting construction crew productivity. Related data has been collected from 117 plastering crews through a systematized time study and comparison of prediction performances of the three methods showed that SOM have a superior performance in predicting plastering crew productivity. [Copyright &y& Elsevier]

The self-organizing map (SOM), which is widely used in economics and engineering applications, is a type of artificial neural network trained without supervision. SOM is used to represent multidimensional data in much lower dimensional spaces-usually in two dimensions (2D)-while preserving the topological properties of the input space. In this study, 2D maps were produced by using SOM to display the relationship between seasons, length, weight, and isopod infestation of goldblotch grouper ( Epinephelus costae Staindahner, 1878). This is first study of gnathiid isopod praniza larvae infesting goldblotch grouper ( E. costae Staindahner, 1878) in the northeast Mediterranean Sea (36°36′ N-36°07′ E, 35°52′ N-36°25′ E) in Iskenderun Bay. Fish were sampled monthly from Iskenderun Bay for a period of 12 months from 2006 May to 2007 April (Nt = 331, $$ {\hbox{Wt}}\pm {\hbox{SD}}\left( {\hbox{range}} \right) = {392}.{92}\pm {72}.{76}\,{\hbox{g}}\left( {{169} - {927}\,{\hbox{g}}} \right) $$, $$ {\hbox{TLt}}\pm {\hbox{SD}}\left( {\hbox{range}} \right) = {3}0.{85}\pm {3}.{88}\,{\hbox{cm}}\left( {{17} - {48}.{3}\,{\hbox{cm}}} \right) $$). Gnathia sp. was only extracted from the epithelium of the buccal cavity and internal side of the gills arch. The monthly patterns in infested fish samples (Np = 109, $$ {\hbox{Wp}}\pm {\hbox{SD}}\left( {\hbox{range}} \right) = {349}.{25}\pm {182}.{79}\,{\hbox{g}}\left( {{169} - {853}\,{\hbox{g}}} \right) $$, $$ {\hbox{TLt}}\pm {\hbox{SD}}\left( {\hbox{range}} \right) = {26}.0{5}\pm {12}.{68}\,{\hbox{cm}}\left( {{18}.{2} - {45}.0\,{\hbox{cm}}} \right) $$) infestation rates (mean prevalence, P = 28.12% (0-60) and mean intensity $$ \left( {{\hbox{MI}}\pm {\hbox{SD}} = {23}.{69}\pm {14}.{78}\left( {{4} - {82}} \right)} \right. $$, the relationship between length-weight and infested/non-infested fish were calculated. Although the gnathiid parasite high intensities were observed in fish, there was no significant effect on the growth and general health condition of infested fish. [ABSTRACT FROM AUTHOR]

Abstract: A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in low-dimensional form without losing topological relations of the data. After an unsupervised learning process, it organizes the data on the basis on similarity. In the current study, a SOM based algorithm has been developed which not only produces 2-D maps to analyze the relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation of the model has been achieved both by using artificial data set and data from 144 concrete pouring, 101 formwork and 101 reinforcement crews. The results show that maps which are produced by the model are satisfactory in clustering the data and prediction performance of the model is superior to similar artificial neural network models. [Copyright &y& Elsevier]