Artificial neural network modelling
Artificial Neural Network Modelling of Traffic Noise in Agra Firozabad Highway
... the network. After several such iterations, the network was trained to give the desired output for a given input ...layer network structure included ten hidden neurons, describing the dynamics of ...
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Artificial neural network modelling approach for assessment of stratified thermal energy storage tank
... system modelling and simulation field due to their learning ability and versatile mapping capabilities ...system modelling including TES ...using artificial neural ...
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Artificial Neural Network Modelling of Vibration in the Milling of AZ91D Alloy
... the modelling was performed by the semi-discreti- zation method which consists in transforming the models and equations of continuous func- tions into their discrete ...
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Pavement Condition Forecasting Through Artificial Neural Network Modelling
... biological neural networks of the human ...A neural network is characterised by its architecture that represents the pattern of connection between nodes, its method of determining the connection ...
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Fault tree analysis and artificial neural network modelling for establishing a predictive ship machinery maintenance methodology
... An artificial neural network consists of interconnection of ...a neural network consists of n layers of neurons of which two are input and output layers, ...The network topology ...
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... Artificial Neural Network modelling methodology for the TIG welding allowed extensive analysis of each input variables for predicting the best possible sets of output parameters, as well as ...
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Optimization of Medium Components Using Artificial Neural Networks
... Methods: Here, we separately investigated effect of K2HPO4, MgSO4, (NH4)2SO4 and NH4CL on maximum growth of bacteria BL21 after transforming BL21 with PET-32α that containing para thyroid hormones gene. Then, the salts ...
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Modelling BOD and COD using Artificial Neural Network with Factor Analysis
... The factor analysis is started by selecting the 24 parameters of the water quality. The monthly data of the year 2006 to 2015 of the Korapuzha river is used for the analysis. The first decision in the factor analysis ...
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An Efficient Modelling Agricultural Production Using Artificial Neural Network (ANN)
... Data mining is the process of extracting important and useful information from large sets of data. The goal of the data mining process is to extract knowledge from an existing data set and transform it into a unique ...
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MODELLING OF SURFACE OZONE USING ARTIFICIAL NEURAL NETWORK IN AN URBAN AREA
... The transfer function used here is the sigmoidal function. The ANN’s are product of the artificial intelligence, which miming the neurons networks, allow expert systems and learning skills. In this respect they ...
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Modelling of Conventional and Severe Shot Peening Influence on Properties of High Carbon Steel via Artificial Neural Network
... Also, very thin layer of UFG were seen under the sharp boundary of severe shot peened specimens. Figure 8 shows the XRD patterns of the surface of severe shot peened specimens. The grain sizes of the related specimens ...
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Rainfall-runoff modelling using artificial neural network method
... Most synthetic procedures for estimating design flood hydrographs are deterministic in that the design flood is derived from a hypothetical design storm. A review of some of the more widely used procedures for estimating ...
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Comparative Study of Artificial Neural Network and Response Surface Methodology for Modelling and Optimization the Adsorption Capacity of Fluoride onto Apatitic Tricalcium Phosphate
... Abstract In this study, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to develop an approach for the evaluation of fluoride adsorption process. A batch ...
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Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models
... This paper employs modular neural networks. These networks are a special class of multi-layer perceptron (MLP) feed-forward artificial neural network model corresponding to input data maps. ...
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Vol 5, No 1 (2013)
... A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of ...
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Optimization of EDM Process Parameters by Using Artificial Neural Network: A Review
... Abstract: - Electrical discharge machining (EDM) is one of the most primitive and categorized non-conventional machining process. Edm is basically based on thermal spark generated between the tool and work piece in the ...
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Height prediction of tectona grandis trees by mixed effects modelling and artificial neural networks
... a neural network to produce adequate outputs for inputs that were not present during training as suggested by Binoti et ...the Artificial Neural Networks method can be found in Bragra et ...
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Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models
... of network depends on the nature of the problem to be solved (Goethals et ...the modelling problem, the input data representation and the form of the network output ...
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Estimation of groundwater level using a hybrid genetic algorithm-neural network
... BP neural network consists of five input variables, seven hidden neurons with hyperbolic tangent function and one output variable with a linear activation function, transform the sum of all the weighted ...
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Using Artificial Neural Network Modeling in Forecasting Revenue: Case Study in National Insurance Company/Iraq
... “artificial neural network with an error backward propagation algorithm” and an “artificial neural network with a genetic algo- rithm”, 61 firms in 7 financial years, from the ...
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