artificial neural network application in control system
a facial expression classification system integrating canny, principal component analysis and artificial neural network
... Rapid Facial Expression Classification Using Artificial Neural Network [10], Facial Expression Classification Using Multi Artificial Neural Network [11] in the same JAFFE database. TABLE IV. ... Expression Classification Using Artificial Neural Networks [10] 73.3% Facial Expression Classification Using Multi Artificial Neural Network [11] 83.0% Proposal System (Canny_PCA_ANN) ... than Rapid Facial Expression Classification Using Artificial Neural Networks [10] and Facial Expression Classification Using Multi Artificial Neural Network [11] (only used ANN). Beside, this...
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backpropagation artificial neural network in c++ - codeproject
... The binary floating point file format is expedient when you have a large amount of data. The data is saved in aseparate file as a sequence of floating point numbers in binary format, using 4 ... the console application for backprop training are optional. You may use them for validationand testing of your network, for input data normalization, and error limits during training process.>ann1dn ... layer:ANNetwork::ANNetwork(const wchar_t *fname);ANNetwork::ANNetwork(int layers_number, int *neurons_per_layer);int nerons_per_layer[4] = {128, 64, 32, 10};ANNetwork *ann = new ANNetwork(4,...
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Tài liệu Neural Network Applications in Intelligent doc
... manufacturing in the future [1, 2]. FIGURE 2.1 Hierarchy of neural network applications in intelligent manufacturing. Neural Network Applications in Intelligent Manufacturing System Modelingand ... developed a neural- network/ expert system for engine fault diagnosis in an integrated steelindustry. A multilayer feedforward neural network was trained with engine fault information includingmaintenance ... preservingthe quality of consolidation. Ding et al. [85] applied a neural network for predicting and controlling a leadscrew grinding process.The neural network was a multilayer neural network...
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ellis, g. (2002). observers in control systems - a practical guide
... supporting increased control- law gainsthrough the reduction of phase lag. The increase of control- law gains will directlyincrease the noise susceptibility of the typical control system. In addition, ... usually to filter noiseinputs. Filters are effective in reducing noise, but when filters are in the control loop,they add phase lag, reducing margins of stability; control- law gains often must bereduced ... 2-29b corresponds to the plot in Figure 2-32 withpeaking. Peaking and ringing are both indicators of inadequate margins of stability.Stability issues will be discussed in Chapter 3.Like the Scope...
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facial expression classification based on multi artificial neural network
... called local training. Phase (2) is to train CNN(s) in GF one-by-one called global training. In local training phase, we will train the SNN1 first. After that we will train SNN2, SNNm. ... local training In the global training phase, we will train the CNN1 first. After that we will train CNN2,…,CNNL. Fig 8. CNN1 global training On the other approach is building the ... it Multi Artificial Neural Network (MANN). 3 Multi Artificial Neural Network apply for image classification 3.1 The proposal MANN model Multi Artificial Neural Network (MANN), applying for...
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Neural Network Toolbox in Matlab
... enter:net=train(net,houseInputs,houseTargets);During training, the following training window opens. This window displays training progress and allows you to interrupt training at any point by clicking Stop Training. ... sections explain how to use three graphical tools for training neural networks to solve problems in function fitting, pattern recognition, and clustering. Neural Network including connections ... vectors into three sets:- 60% are used for training.- 20% are used to validate that the network is generalizing and to stop training before overfitting. Fitting a Function1-13Using the Neural...
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engineering vibration analysis with application to control systems (c beards )
... of the system, from the control of factory heating levels to satellite tracking, or from engine fuel control to controlling sheet thickness in a steel rolling mill, there is continual effort ... process of increasing excitation with reducing machine mass and damping has continued at an increasing rate to the present day when few, if any, machines can be designed without carrying out ... machinery, structures, and dynamic systems are also increasing, so that the dynamic performance requirements are always rising. Engineering Vibration Analysis with Application to Control Systems...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 1 pdf
... neural networks; the second volume contains artifi cial neural network applications in industrial and control engineering. This second volume begins with a part of artifi cial neural network applications ... power systems, and power allocation systems, mechanical engineering such as engines and machines, control and robotic engineering such as nonlinear system control, induction motors, system ... application of ANN in textiles and clothing industries will be addressed in last section. Artificial Neural Networks - Industrial and Control Engineering Applications 6 neural net produced...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 2 doc
... as the input units of the neural network. They used a back propagation neural network by eight defect samples for off line training. The initial learning rate was 0.1; keeping reducing to 0.01 ... segmenting pills appearing on converted gray images, extracting of a pill's feature index, and finally assessing pilling grade by Kohonen self Artificial Neural Networks - Industrial and Control ... beyond 3 mm, breaking strength, breaking Artificial Neural Networks - Industrial and Control Engineering Applications 24 Study Area No Title Author Journal Year Vol(No),pp. Findings Limitations...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 3 doc
... percentage Artificial Neural Networks - Industrial and Control Engineering Applications 88 Sao, K.P. & Jain, A. K. (1995). Mercerization and crimp formation in jute. Indian Journal ... percentage compression by ANN model Artificial Neural Networks - Industrial and Control Engineering Applications 74 Tenacity in the machine direction Tenacity in the transverse direction Predicted ... fabric samples and corresponding experimental values of compression properties shown in (Table 12). Artificial Neural Networks - Industrial and Control Engineering Applications 66 nature....
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Artificial Neural Networks Industrial and Control Engineering Applications Part 4 pot
... training2ndANN training3rdANN training4thANN training5thANN trainingRandomly initialized weights & biasesWeights & biases from the 1sttraining1st training ... description of artificial neural network configuration Artificial Neural Networks - Industrial and Control Engineering Applications 120 , where xi is the input of node j of the input layer, ... step training; (b) – in the beginning of the 2nd step training; (c) – at the end of the training. On each screenshot: the menu on the left defines training parameters; the graph in middle-top...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 5 pdf
... 14.14GPa which is corresponding to the sintering temperature of 1420°C and the holding time of 80min, while Artificial Neural Networks - Industrial and Control Engineering Applications 158 2.5 ... Neural Networks - Industrial and Control Engineering Applications 146 According to the BP neural network model, the number of hidden neurons is initially chosen as 6, so the neural network ... algorithm in the optimization of hot pressing parameters Artificial Neural Networks - Industrial and Control Engineering Applications 162 The depicting effect of mentioned factors and their interactions...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 6 potx
... (as-received) Artificial Neural Networks - Industrial and Control Engineering Applications 168 Botlani-Esfahani. M, Toroghinejad. M. R. and Abbasi. Sh. (2009b) Artificial Neural Network Modeling the ... Precipitation in North Carolina. Water, Air, and Soil Pollution, 172, 167. Artificial Neural Networks - Industrial and Control Engineering Applications 176 for testing. For the training stage, ... Artificial Neural Networks - Industrial and Control Engineering Applications 182 Karacan, C.O. (2007). Development and application of reservoir models and artificial neural networks...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 7 pptx
... propagation error. Learning of the network was carried out using 9 data points from the Artificial Neural Networks - Industrial and Control Engineering Applications 228 networks is given by ... 150-158(9) Part 3 Food Industry Artificial Neural Networks - Industrial and Control Engineering Applications 232 ANN was investigated, is given in Table 2. In regard to carcass classification ... discussed, in order to combine analytical prior knowledge with the learning capabilities of Artificial Neural Networks (ANN). The intelligent modeling approach of models employing Artificial Neural Network...
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Artificial Neural Networks Industrial and Control Engineering Applications Part 8 pptx
... improve the training speed of the network[ 22]. The BP neural network training process used in this article is shown in Fig. 3. Fig. 3. The training flow chart of BPNN Input training samples ... shown in Table 3. Fig. 8. Actual structure of neural network. Artificial Neural Networks - Industrial and Control Engineering Applications 246 abandon various kinds of irrational points ... trained, if the NN average error is smaller than the setting object for ending the training, the training is over, or else it keeps on new training after updating the total training steps. Application...
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