[PDF] Top 20 Artificial Neural Networks in the Demand Forecasting of a Metal Mechanical Industry
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Artificial Neural Networks in the Demand Forecasting of a Metal Mechanical Industry
... Benkouider, F., L. Hamami and A. Abdellaoui, 2014. Optimized neural networks using principal component analysis for automatic road extraction from remote sensing. J. Eng. Appl. Sci., 9: 427-433. Bousqaoui, ... See full document
7
Demand Forecasting in Deregulated Electricity Markets
... ahead demand forecasting is essential for the efficient operation of electricity companies in the competitive electricity ...robust demand forecasting tool for the efficient power system ... See full document
6
FORECASTING OF DEMAND USING ARTIFICIAL NEURAL NETWORK FOR SUPPLY CHAIN MANAGEMENT
... The demand forecasting technique which is modeled by artificial intelligence approaches using artificial neural ...in forecasting the future demand and the accuracy of the ... See full document
8
A Study on the Building Demand Forecasting For the Erode Town, Tamilnadu, India
... Electricity Demand Forecast Using Regression Analysis and Artificial Neural Networks based on Principal Components, ICTACT Journal on Soft Computing, july 2012, Volume: 02, ISSUE: 04 ... See full document
11
Irradiance and Demand Forecasting Using Neural Networks
... the industry practice, to predict weather normalized load, which will take place for average annual peak weather conditions and worse than average peak weather condition for a given ...or artificial ... See full document
7
A Review of Epidemic Forecasting Using Artificial Neural Networks
... The number of input nodes corresponds to a number of variables in the input vector used to forecast future values. Too few or too many input nodes can affect learning or prediction capability of the network. The sizes of ... See full document
12
Artificial Neural Network Models Investigation for Euphrates River Forecasting & Back Casting
... flow forecasting . Two SSANN models for forecasting the Euphrates river flow at d/s Al-Hindyaha barrage(IRQ- E3) showed good results, the best one was with three inputs which are the monthly flow data for ... See full document
15
Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA)
... Many theoretical and experimental works have shown that a single hidden layer (with one or more several hidden nodes) is sufficient for ANN to approximate any complex nonlinear function (Dreiseitl and Ohno-Machado, 2002; ... See full document
13
The Usefulness of Artificial Neural Networks in Forecasting Exchange Rates
... Because exchange rates are influenced by many economic, political and psychological factors, it has been hard to identify a unique economic model that can provide reliable forecasts. Some authors state that “the poor ... See full document
8
The Cost Forecasting Application in an Enterprise with Artificial Neural Networks
... the rates of wastage in machines and of maintenance/repair fall and as efficiency and rate of capacity use increase. Maintenance activities are directly related to these parameters. One of the most complex parts of the ... See full document
5
Evaluation of Artificial Neural Networks in Foreign Exchange Forecasting
... The objective of training is to find the set of weights between the neurons that determine the global minimum of error function. Unless the model is over fitted the set of weights should provide good generalization. The ... See full document
8
Advanced approach to numerical forecasting using artificial neural networks
... The RBF-NN has same topology as three layers MLP networks. The main diff erence is in the hid- den layer. As defi ned in (Wedding, Cios; 1996), (Šíma, Neruda; 1996) the hidden layer of nodes each node represents a ... See full document
8
Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand
... of different techniques with ANN has been successfully applied to both short term and long term energy demand forecasting. Hence, several variants of ANN which are generally hybridization of neural ... See full document
35
Monthly runoff forecasting by means of artificial neural networks (ANNs)
... that in the case of MLP models the best performance was achieved by the two and four cluster MLP models with the two cluster models doing somewhat better. It is worthy to mention that although the four cluster MLP models ... See full document
11
Topological optimisation of artificial neural networks for financial asset forecasting
... better forecasting performance, in more general cases (including the results from robustness test in Section ...the forecasting performance is positively correlated to the testing sample ... See full document
179
FORECASTING OF DAILY NEED PRODUCT USING ARTIFICIAL NEURAL NETWORKS
... GFF networks, train certain output nodes to respond to certain input patterns and the changes in connection weights, due to learning, cause those same nodes to respond to more general classes of ... See full document
8
Artificial Neural Network Approach for Load Forecasting in Demand Side Management
... load forecasting has been adopted to improve the energy efficiency without compromising with consumer’s ...using neural network. The neural network is trained by actual load data and it has predicted ... See full document
6
Inflation Forecasting in Pakistan using Artificial Neural Networks
... While each neuron is, in and of itself, a computational unit, neurons may be combined into layers to create complex but efficient groups that can learn to distinguish between patterns within a set of given inputs. ... See full document
19
Overview of the Artificial Neural Networks and Fuzzy Logic Applications in Operational Hydrological Forecasting Systems
... Better forecasting floods and with a larger lead time, is the main sustainable way of adapting to and managing such ...flood forecasting and warning systems have several limitations, such as, insufficient ... See full document
6
A hybrid approach based on arima and artificial neural networks for crime series forecasting
... Forecasting is a process of predicting or estimating the future events by referring to historical data. The information provided about the potential future events and their consequences is able to make important ... See full document
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