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Input Vector (input + vector)
Selected AbstractsA data-driven algorithm for constructing artificial neural network rainfall-runoff modelsHYDROLOGICAL PROCESSES, Issue 6 2002K. P. Sudheer Abstract A new approach for designing the network structure in an artificial neural network (ANN)-based rainfall-runoff model is presented. The method utilizes the statistical properties such as cross-, auto- and partial-auto-correlation of the data series in identifying a unique input vector that best represents the process for the basin, and a standard algorithm for training. The methodology has been validated using the data for a river basin in India. The results of the study are highly promising and indicate that it could significantly reduce the effort and computational time required in developing an ANN model. Copyright © 2002 John Wiley & Sons, Ltd. [source] On the use of reactive power as an endogenous variable in short-term load forecastingINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 5 2003P. Jorge Santos Abstract In the last decades, short-term load forecasting(STLF) has been the object of particular attention in the power systems field. STLF has been applied almost exclusively to the generation sector, based on variables, which are transversal to most models. Among the most significant variables we can find load, expressed as active power (MW), as well as exogenous variables, such as weather and economy-related ones; although the latter are applied in larger forecasting horizons than STLF. In this paper, the application of STLF to the distribution sector is suggested including inductive reactive power as a forecasting endogenous variable. The inclusion of this additional variable is mainly due to the evidence that correlations between load and weather variables are tenuous, due to the mild climate of the actual case-study system and the consequent feeble penetration of electrical heating ventilation and air conditioning loads. Artificial neural networks (ANN) have been chosen as the forecasting methodology, with standard feed forward back propagation algorithm, because it is a largely used method with generally considered satisfactory results. Usually the input vector to ANN applied to load forecasting is defined in a discretionary way, mainly based on experience, on engineering judgement criteria and on concern about the ANN dimension, always taking into consideration the apparent (or actually evaluated) correlations within the available data. The approach referred in the paper includes pre-processing the data in order to influence the composition of the input vector in such a way as to reduce the margin of discretion in its definition. A relative entropy analysis has been performed to the time series of each variable. The paper also includes an illustrative case study. Copyright © 2003 John Wiley & Sons, Ltd. [source] Neural networks,a new approach to model vapour-compression heat pumpsINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 7 2001H. Bechtler Abstract The aim of this paper is to model the steady-state performance of a vapour-compression liquid heat pump with the use of neural networks. The model uses a generalized radial basis function (GRBF) neural network. Its input vector consists only of parameters that are easily measurable, i.e. the chilled water outlet temperature from the evaporator, the cooling water inlet temperature to the condenser and the evaporator capacity. The model then predicts relevant performance parameters of the heat pump, especially the coefficient of performance (COP). Models are developed for three different refrigerants, namely LPG, R22 and R290. It is found that not every model achieves the same accuracy. Predicted COP values, when LPG or R22 are used as refrigerant, are usually accurate to within 2 per cent, whereas many predictions for R290 deviate more than ±10 per cent. Copyright © 2001 John Wiley & Sons, Ltd. [source] A review on the use of the adjoint method in four-dimensional atmospheric-chemistry data assimilationTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 576 2001K.-Y. Wang Abstract In this paper we review a theoretical formulation of the adjoint method to be used in four-dimensional (4D) chemistry data assimilation. The goal of the chemistry data assimilation is to combine an atmospheric-chemistry model and actual observations to produce the best estimate of the chemistry of the atmosphere. The observational dataset collected during the past decades is an unprecedented expansion of our knowledge of the atmosphere. The exploitation of these data is the best way to advance our understanding of atmospheric chemistry, and to develop chemistry models for chemistry-climate prediction. The assimilation focuses on estimating the state of the chemistry in a chemically and dynamically consistent manner (if the model allows online interactions between chemistry and dynamics). In so doing, we can: produce simultaneous and chemically consistent estimates of all species (including model parameters), observed and unobserved; fill in data voids; test the photochemical theories used in the chemistry models. In this paper, the Hilbert space is first formulated from the geometric structure of the Banach space, followed by the development of the adjoint operator in Hilbert space. The principle of the adjoint method is described, followed by two examples which show the relationship of the gradient of the cost function with respect to the output vector and the gradient of the cost function with respect to the input vector. Applications to chemistry data assimilation are presented for both continuous and discrete cases. The 4D data variational adjoint method is then tested in the assimilation of stratospheric chemistry using a simple catalytic ozone-destruction mechanism, and the test results indicate that the performance of the assimilation method is good. [source] Area efficient layouts of the Batcher sorting networks ,NETWORKS: AN INTERNATIONAL JOURNAL, Issue 4 2001Shimon Even Abstract In the early 1980s, the grid area required by the sorting nets of Batcher for input vectors of length N was investigated by Thompson. He showed that the ,(N2) area was necessary and sufficient, but the hidden constant factors, both for the lower and upper bounds, were not discussed. In this paper, a lower bound of (N , 1)2/2 is proven, for the area required by any sorting network. Upper bounds of 4N2 and 3N2 are shown for the bitonic sorter and the odd,even sorter, respectively. In the layouts, which are presented to establish these upper bounds, slanted lines are used and there are no knock-knees. © 2001 John Wiley & Sons, Inc. [source] Nutrient transport within and between habitats through seed dispersal processes by woolly monkeys in north-western AmazoniaAMERICAN JOURNAL OF PRIMATOLOGY, Issue 11 2010Pablo R. Stevenson Abstract The contribution of vertebrate animals to nutrient cycling has proven to be important in various ecosystems. However, the role of large bodied primates in nutrient transport in neotropical forests is not well documented. Here, we assess the role of a population of woolly monkeys (Lagothrix lagothricha lugens) as vectors of nutrient movement through seed dispersal. We estimated total seed biomass transported by the population within and between two habitats (terra firme and flooded forests) at Tinigua Park, Colombia, and quantified potassium (K), phosphorus (P) and nitrogen (N) content in seeds of 20 plant species from both forests. Overall, the population transported an estimated minimum of 11.5 (±1.2 SD),g of potassium, 13.2 (±0.7),g of phosphorus and 34.3 (±0.1),g nitrogen, within 22.4 (±2.0),kg of seeds ha,1,y,1. Approximately 84% of all nutrients were deposited in the terra firme forest mostly through recycling processes, and also through translocation from the flooded forest. This type of translocation represents an important and high-quality route of transport since abiotic mechanisms do not usually move nutrients upwards, and since chemical tests show that seeds from flooded forests have comparatively higher nutrient contents. The overall contribution to nutrient movement by the population of woolly monkeys is significant because of the large amount of biomass transported, and the high phosphorus content of seeds. As a result, the phosphorus input generated by these monkeys is of the same order of magnitude as other abiotic mechanisms of nutrient transport such as atmospheric deposition and some weathering processes. Our results suggest that via seed dispersal processes, woolly monkey populations can contribute to nutrient movement in tropical forests, and may act as important nutrient input vectors in terra firme forests. Am. J. Primatol. 72:992,1003, 2010. © 2010 Wiley-Liss, Inc. [source] | ||||||||||