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Analysis and prediction of the fluctuation of sardine abundance using a neural network ArchiMer
Aoki, I; Komatsu, T.
This paper examines the use of a neural network to analyse and predict the winter catch, in the Joban-Boso Seas off the Pacific coast of central Japan, of young Japanese sardine (Sardinops melanostictus) representing the index of recruits in the sardine stock. The supervised learning paradigm, a three-layer network and a back-propagation algorithm were employed in constructing the neural net. A number of biological, hydrographic and climatic factors constituted an input vector, the output being the catch of young sardine. The association of sardine abundance with environmental factors was quantified in the form of the trained neural network, which indicated important associations with the Southern Oscillation Index, with patterns of the Kuroshio and the...
Tipo: Text Palavras-chave: Neural network; Japanese sardine; Recruit; Climatic change; Kuroshio-Oyashio.
Ano: 1997 URL: http://archimer.ifremer.fr/doc/00093/20436/18103.pdf
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Application of a More Advanced Procedure in Defining Morphological Types International Journal of Morphology
Jaksic,Damjan; Lilic,Ljubisa; Popovic,Stevo; Matic,Radenko; Molnar,Slavko.
It is well known that the most evident differences in humans are those related to anthropometric characteristics, and that during continuous monitoring the relation between human behavior and human abilities concerning their anthropometric characteristics was observed. The aim of this study was to detect and define the morphological types with the use of slightly different and more advanced methodologies. The sample included 149 male subjects, first-year students of the Faculty of Sport and Physical Education in Novi Sad, using an anthropometric measurement technique. A total of 12 anthropometric measures, defined according to the four-dimensional morphological model was used. For all variables, basic descriptive statistics were calculated while student...
Tipo: Journal article Palavras-chave: Neural network; Intruder; Students; Anthropometry; Somatotypology.
Ano: 2014 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-95022014000100019
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Comparative study of acoustic signals of rolling eggs on inclined plate and impulse response in eggshell crack detection CIGR Journal
Lashgari, Majid; Mohammadigol, Reza.
The potential of acoustic signals of rolling eggs on an inclined plate and impulse response for nondestructive detection of eggshell crack was investigated. Discrimination of hairline cracked and star cracked eggs from intact ones were carried out using artificial neural network. Ten features were used based on one-way ANOVA F-test statistics. According to the result, holdout detection accuracy of inclined plate and impulse response methods were 92.3% and 94.6%, respectively. The results indicated that these two methods were potentially useful for discrimination of eggs according to detection of different eggshell cracks.
Tipo: Info:eu-repo/semantics/article Palavras-chave: Eggshell crack; Inclined plate; Impulse response; Neural network; Classification.
Ano: 2018 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/4140
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Máquinas de soporte vectorial en el análisis de series de tiempo. Colegio de Postgraduados
Rivera Castillo, Enrique.
La evapotranspiración de referencia (ETo) es un proceso no lineal empleado para determinar la cantidad de agua utilizada en los programas de irrigación. El nivel de precisión de esta variable a partir de datos históricos, ha sido siempre fundamental. En este trabajo, se presenta una aplicación de las Máquinas de Soporte Vectorial (SVMs) para la predicción de ETo y se compara su capacidad predictiva con otras dos metodologías de predicción: Redes Neuronales Artificiales de Multicapa (MLP) y modelos Autoregresivos Integrados de Promedio Móvil (ARIMA). Se propone un algoritmo heurístico de refinamiento para la implementación de las SVM resultando en una predicción mucho mejor que la obtenida con los otros dos métodos. La capacidad de predicción fue evaluada...
Palavras-chave: Evapotranspiración; Red neuronal; Predicción; Máquina de soporte vectorial; Evapotranspiration; Neural network; Forecasting; Support vector machine; Estadística; Maestría.
Ano: 2012 URL: http://hdl.handle.net/10521/1693
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Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model AgEcon
Guo, Qiang; Luo, Chang-shou; Wei, Qing-feng.
Considering the complexity of vegetables price forecast, the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm by using the characteristics of genetic algorithm and neural work. Taking mushrooms as an example, the parameters of the model are analyzed through experiment. In the end, the results of genetic algorithm and BP neural network are compared. The results show that the absolute error of prediction data is in the scale of 10%; in the scope that the absolute error in the prediction data is in the scope of 20% and 15%. The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model, especially the absolute error of prediction data is within the scope of 20%....
Tipo: Journal Article Palavras-chave: Genetic algorithm; Neural network; Vegetables price; Prediction; China; Agribusiness.
Ano: 2011 URL: http://purl.umn.edu/117430
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SINGLE-PASS SEA/ICE DISCRIMINATION USING ERS-2 SCATTEROMETER DATA Gayana
Neyt,Xavier; Pettiaux,Pauline; Manise,Nicolas; Acheroy,Marc.
This paper presents a new method to perform sea/ice discrimination in single-pass ERS-2 scatterometer data. Existing methods are 1rst reviewed and compared in a consistent framework. Next, the ice probability according to the individual existing methods is learned through the use of a neural network. Finally, the individual criteria are combined together in order to increase the sea-ice discrimination accuracy. The proposed method is shown to provide an acceptable performance even on single-pass data, i.e., without requiring temporal averaging
Tipo: Journal article Palavras-chave: Scatterometry; Ice discrimination; Neural network.
Ano: 2004 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-65382004000300021
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Utilization of new computational intelligence methods to estimate daily Evapotranspiration of wheat using Gamma pre processing CIGR Journal
mohammadigolafshani, nima; koulaian, ali.
Estimation of evapotranspiration (ET) is needed in water resources management, scheduling of farm irrigation, and environmental assessment. Hence, in practical hydrology, it is often crucial to reliably and constantly estimate evapotranspiration. Accordingly, 3 artificial intelligence (AI) techniques comprising adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference- wavelet (ANFIS-Wavelet) were applied in to estimate wheat crop evapotranspiration (ETc). A case study in a Dashtenaz region located in Mazandaran, Iran, was conducted with weather daily data, including maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, wind speed, and solar radiation since...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Adaptive neuro-fuzzy inference system; Adaptive neuro-fuzzy inference-wavelet; Evapotranspiration; Neural network; Wheat.
Ano: 2018 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/4459
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