parameters artificial neural networks as the vver 1000 core predictor
Estimation of Proper Strain Rate in the CRSC Test Using a Artificial Neural Networks
... rate, where the results show a similar tendency With the result, the preconsolidation pressure ratio increases as the increase of strain rate and their trends are similar to those of the previous ... research In the case of sample D, the predicted values of ANN model are slightly different from the field data In particular, these differences are increase at the high strain rate range The reason ... data of the consolidation test results are used 43 learning data are used for training the ANN model, and the others are used for the comparison 631 16 between the predicted value and the measured...
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Forecasting with artificial neural networks: The state of the art pot
... forecasting as used in the Box-Jenkins model in which the forecast values are iteratively used as inputs for the next forecasts In this case, only one output node is necessary The second called the ... factors: the noise in the data and the underlying model, then the accuracy limit of a linear model such as the Box-Jenkins is determined by the noise in the data and the degree to which the underlying ... forecasting may be better for the following two reasons First, the neural network can be built directly to forecast multi-step-ahead values It has the benefits over the iterative method like the...
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ARTIFICIAL NEURAL NETWORKS – ARCHITECTURES AND APPLICATIONS doc
... who has a prosthetic device to allow them to better manipulate it Since the brain is usually multitasking, the researchers will have to pick up the signal for the desired task from all the other ... noise was given during t=1∼ 500 Figure 16 shows a part of the association result of the propsoed model when the pattern “crow” with 20% noise was given t=501∼ 1000 As shown in these figures, the ... the proposed model when the pattern “bear” was given during t=1∼ 500 Figure 20 shows a part of the association result of the proposed model when the pattern “mouse” was given t=501∼ 1000 In these...
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Artificial Neural Networks - a Useful Tool in Air Pollution and Meteorological Modelling pdf
... applications The two selected are the Multilayer Perceptron artificial Neural Network (MPNN) and the Kohonen neural network (KNN) Both can be replaced by other artificial neural networks for the same ... take the values from the model input variables and pass the values to the neurons in the hidden layer, the hidden layer neurons pass the values to the higher hidden layers and finally to the output ... are the basic parameters of the model that are adjusted during the learning process Model inputs take their values from the input features – measured parameters that determine the output of the...
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vehicle signal analysis using artificial neural networks
... accuracy for the estimating gross vehicle weight (GVW); however the accuracy decreased for individual axle weights [10] The application of artificial neural networks (ANN) to the B-WIM was attempted ... of the ANN Structure The conventional influence line theory first calculates axle weight directly from the measured signal and then the GVW is derived by summing axle weights In this study, the ... being applied as the Free Axle Detector (FAD) or the Nothing On Road (NOR) B-WIM system [3,4] Since most of the existing B-WIM systems are developed on the static influence line theory, the accuracy...
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a new type of structured artificial neural networks
... initialized by the C in that column Then, the data travels down the scope to the various A’s in column j, and then horizontally from the A’s to the C’s in the corresponding rows, reaching as far as the ... with the mathematical rigor and accuracy of traditional computers and the interoperability of the MMC by implementing some services as ANNs and the rest as CPUs The theorem presented in the next ... neurons are associated with the time intervals where they fire, and the classes would be neural cliques [11] Table summarizes these observations Figure The set of points for the example The given...
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audio to visual speech synthesis using artificial neural networks
... make catastrophic errors and therefore misguide the visible speed synthesis The mapping between the acoustic parameters and the visible speech parameters is continuous and a slight error in the analysis ... the corresponding training values The average correlation of all parameters was also used as an indicator of network performance The networks varied somewhat in their abilities to reproduce the ... speaker The facial movements are realistic because they have been fine-tuned to resemble a natural talker as much as possible [9] These control parameters then serve as labeled representation of the...
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báo cáo hóa học: " Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks" doc
... Figure The increase of the RMSE due to the spasms, evidenced by the lines crossing each column in Figure 7, was very similar for the three controllers in the early spasms as well as in the later ... of the mass In this case, while an overshooting was shown at the first cycle, once the error was detected by the feedback, NF correction reduced the error (asterisks lower than circles) On the ... changing parameters: damping, Trec, Tfat and the limb mass The horizontal axis indicates the percent by which the parameter has been varied, the vertical axis indicates the RMSE error The errors in the...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 1 pdf
... explicit features Then the supervised ANN was employed to classify these fibers, based on the features extracted with image processing and unsupervised artificial neural networks The classification ... and the neural network technique using either BP or radial basis (RB) was effective for classifying the fabric stitching defects By using the BP neural network, the recognition rate was 100% The ... used feedforward and Kohonen networks The other types of artificial neural networks such as recurrent neural network, associative neural network and dynamic neural networks (refer to http://en.wikipedia.org/wiki/Types_of _artificial_ neural_ networks...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 2 doc
... the powders as well as the density of the probe liquids together with the capillary rise time of the liquids in the corresponding powders The viscosity predictor network had surface tension as ... performance The result was measured by correct classification rate (CCR) which was defined as the number of correctly classified images divided by the number of total images They found that ANN had the ... score which was then used to analyze the overall performance of the yarns by those two models The appearance of the spliced yarns was expressed as the retained yarn appearance (RYA) which was...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 3 doc
... punches/cm2 was given on each passage of the web, changing the web face alternatively The fabric samples were produced as per the variables presented in Table 68 Artificial Neural Networks - Industrial ... attempt has also been made to study the effect of number of hidden layer in neural network model The highest correlation has been found in artificial neural network with three hidden layers The neural ... Properties Using Artificial Neural Network 67 training phase The three hidden layered artificial neural network models take more time for computation during training phase but the predicted results...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 4 pot
... variations in the parameters governing the manufacturing process On the other hand, yet, the high costs of fatigue tests as compared with those of the simple monotonic tests, as well as the need for ... 700 1000 Table Networks details and architectures of n' As mentioned earlier, the performance of the networks was evaluated by calculating MSE errors In order to assess the validity of the networks ... Table Networks details and architectures of K' As mentioned previously, the performance of the networks was evaluated by calculating the MSE errors In order to assess the validity of the networks...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 5 pdf
... -0.59 Based on the above 59 parameters and L=3+7d, the number of hidden layer neurons is ascertained as Therefore the structure of BP neural network is 3×8×3 and the last 11 parameters are the threshold ... 0.15 Based on the above 39 parameters and L=3+6d, the number of hidden layer neurons is ascertained as Therefore, the structure of BP neural network is 2×6×3 and the last parameters are the threshold ... Xi is the actual input of the neuron i of the input layer, Wij is the connection weight from the neuron i of the input layer to the neuron j of the hidden layer, θj is the threshold of the neuron...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 6 potx
... is the cross-section of the iron core A measurement coil is wound around the iron core for measurement purposes The primary and measurement coils have the same number of turns N Fig The iron core ... vector for the each individual neuron The artificial neural networks accumulate the knowledge during the training process, while the effectiveness of the artificial neural network depends on the quality ... is normally measured by the Rogowski coil (Ramboz, 1996), the iron core flux density can be measured by the Hall sensor or by a probe coil wound around the iron core In the case, the flux density...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 7 pptx
... used to classify the shape and used as the input parameters of the network The sorting process was controlled by computer software which was well designed and created on visual basic 6.0 The experiments ... affecting the attrition: the larger the agglomerate size and higher the water activity, higher was the attrition index under the same tap number The Hausner ratio was well correlated with the attrition ... lamb carcasses) is performed at the end of the slaughter line and represents a basis for the payment to the farmer Another example is in poultry, where the carcasses are inspected at the slaughter...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 8 pptx
... factors, such as the size of the network, the need of the network test and the distribution of the input-output and so on The size of the network is the most important, and ordinarily the larger ... gives the connection between the measurement ( zk ) and the state vector ( x k ) The sampled value of the signal is considered to be the sum of the real components of the harmonic phasors and the ... seen from chart 1, the average error of the output from the neural network to neural networks is gradually reduced, but it begin to increase from the network It shows in the same case of input samples...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 9 pptx
... that stage, the weights in connections between the input layer and the hidden layer and the parameters of the radial basis functions of the hidden layer are already set as determined in the first ... occurred The same network setting parameters is used for training the other networks 3.1.3 Pre-testing and simulation After the networks have been trained, next step is to simulate the network The ... train the ANN Then the descriptions of inputs and outputs of the training data for the ANN are easily obtained from the load flow results and each method used as teachers respectively The proposed...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 10 doc
... over the measured signal The early phase of the signal is well predicted However, in the second phase of the characteristic the prediction data starts oscillating in high-frequencies as well as ... order to ease the training process of the NLARX model In view of the data variability the sets are normalized to reduce the range of the inputs data Then a further step of processing is done as follows ... structures in these investigations have been the NLARX as has also presented in the example application in the previous section The NLARX structure can accommodate the dynamics of the system by...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 11 pptx
... Table 10 ANN model parameters for carbide tool The trained neural network was tested based on the second half of the input-output samples in Tables and The performance of the best training processes ... SFF and the values predicted by ANN for the two types of the tools used in the demonstration From the tables it can be seen that the obtained values closely matches the predicted values of the ANN ... On the inner surface there are two heating spirals (powered by voltage 230 V) In 360 Artificial Neural Networks - Industrial and Control Engineering Applications the middle of the cylinder there...
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