Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruct…
2017
Abstract Accurate wind speed forecasting is crucial to reliable and secure power generation system. However, the intermittent and unstable nature of wind speed makes it very difficult to be predicted accurately. This paper proposes a novel hybrid model based on variational mode decomposition (VMD), phase space reconstruction (PSR) and wavelet neural network optimized by genetic algorithm (GAWNN) for multi-step ahead wind speed forecasting. In the proposed model, VMD is firstly applied to disassemble the original wind speed series into a number of components in order to improve the overall prediction accuracy. Then, the multi-step ahead forecasting for each component is conducted using GAWNN…
Influence of raw data analysis for the use of neural networks for win farms productivity prediction
2011
In the last decade wind energy had a strong growth because of cost effectiveness of the technology and the high remunerative of investments.
Generalized feed-forward based method for wind energy prediction
2013
Abstract Even though a number of new mathematical functions have been proposed for modeling wind speed probability density distributions, still the Weibull function continues to be the most commonly used model in the literature. Therefore, the parameters of this function are still widely used to obtain typical wind probability density distributions for finding the wind energy potential by researchers, engineers and designers. Once long-term average of Weibull function’s parameters are known, then the probability density distributions can easily be obtained. Artificial neural network (ANN) can be used as alternative to analytical approach as ANN offers advantages such as no required knowledg…
Urban public transport optimization by bus ways: a neural network-based methodology
2007
This paper describes an approach for planning the introduction of bus lanes into the urban road network, that has been applied to the urban area of Palermo. The proposed modelling tool adopts a multi-agent objective function expressing the trade-off between the interests of diverse stakeholders: the generalized transport cost for car drivers and the travel time for public transport users. The reaction of car traffic to a certain planning scenario has been simulated by the DUE assignment technique and the positive impact of the modal shift on the objective function has been tackled by attaching a suitable weight to the time saving for bus passengers. The rise in the bus travel speed, owing t…
Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques
2021
A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contamin…
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility.
2020
Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total …
Neural Modeling of Greenhouse Gas Emission from Agricultural Sector in European Union Member Countries
2018
The present paper discusses a novel methodology based on neural network to determine agriculture emission model simulations. Methane and nitrous oxide are the key pollutions among greenhouse gases being a major contribution to climate changes because of their high potential global impact. Using statistical clustering (k-means and Ward’s method), five meaningful clusters of countries with similar level of greenhouse gases emission were identified. Neural modeling using multi-layer perceptron networks was performed for countries placed in particular groups. The parameters that characterize the quality of a network are the predictive errors (mainly validation and test) and they are high (0.97–…
Remaining useful life estimation of HMPE rope during CBOS testing through machine learning
2021
Fibre rope used in cranes for offshore deployment and recovery has significant potential to perform lifts with smaller cranes and vessels to reach depths limited by weight of steel wire rope. Current condition monitoring methods based on manual inspection and time-based and reactive maintenance have significant potential for improvement coupled with more accurate remaining useful life (RUL) prediction. Machine learning has found use as a condition monitoring approach, coupled with vast improvements in data acquisition methods. This paper details data-driven RUL prediction methods based on machine learning algorithms applied on cyclic-bend-over-sheave (CBOS) tests performed on two fibre rope…
Modelling PM10 Crisis Peaks Using Multi-agent Based Simulation: Application to Annaba City, North-East Algeria
2015
The paper describes a MAS (multi-agent system) simulation approach for controlling PM10 (Particulate Matter) crisis peaks. A dispersion model is used with an Artificial Neural Network (ANN) to predict the PM10 concentration level. The dispersion and ANN models are integrated into a MAS system. PM10 source controllers are modelled as software agents. The MAS is composed of agents that cooperate with each other for reducing their emissions and control the air pollution peaks. Different control strategies are simulated and compared using data from Annaba (North-East Algeria). The simulator helps to compare and assess the efficiency of policies to control peaks in PM10.
Improving Pairs Trading Using Neural Network Techniques and Fundamental Ratios
2020
Pairs trading is a quantitative trading strategy consisting on identifying two stocks that historically move together and, using the assumption that their prices difference has mean- reverting properties, exploit the deviation from the mean by taking long – short position in the chosen pair to profit. Throughout the years, different approaches have been developed in order to exploit this strategy. However, there is little literature who looks whether the divergences in the prices are generated by poor company results, i.e. whether the deviation from the mean are product of bad (or good) fundamentals and are justified, or if they generate a new equilibrium point for the pair. In addition, si…