... candidate text regions. Those features are used as the input of a neural network for training based on the back-propagation algorithm for neural networks. After the neuralnetwork is well trained, ... on a neural network. The training of the neuralnetwork is based on the features we obtain from the DWT detail component sub-bands. As shown in Figure 6, the proposed neural network architecture ... a method for extracting text regions from static images or video sequences using DWT and a neural network. DWT provides features of text regions for the training of a neuralnetwork using...
... average color and colors larger than the average color. Basically these features represent the central positions (average color) and the standard errors of three parts: the whole histogram, ... having color smaller than the average and pixels having color larger than the average accordingly. The next three are the standard errors of number of pixels for all colors; colors smaller than ... Classification result Figure 9. Binarization result IV. CONCLUSION AND FUTURE WORKS A neuralnetwork based method is discussed in this paper. The features used for the neuralnetwork are not only...
... lớp neuron.Một lớp neuronTrong hình dưới mô tả một lớp neuron với : R : số phần tử của vectơ đầu vào S : số neuron trong lớpa : vector ngõ ra của lớp neuronHàm truyềnCó r t nhiều hàm truyền ... truyền áp dụng trong Neural Networks, trong đó ba hàm thường sử dụng nhấtlà Hard Limit, Linear, Log-Sigmoid.Tổng quát với một hàm truyền có đầu vào là một hoặc một nhóm vector thì đầu ra là a = ... quan trọng và chúng không ảnh hưởng lẫn nhau. Kiểu đầu vào xảy ra liên tục được mô phỏng trong mạng dynamic. Thứ tự các giá trị đầu vào r t quan trọng. Trong trường hợp này, giá trị đầu ra thu...
... nonlinear dynamic system theory that incorporates neural networksis still an ongoing research area.1.3.1.2 Nonlinear FilteringThere are many reports on using artificial neural networks to perform ... using neural networks. In the first part, in-depth surveys of recent progress of neuralnetwork computingparadigms are presented. Part One consists of five chapters:• Chapter 1: Introduction to Neural ... different kinds of nonlinear networks with the same theoretical patternstorage (or pattern memorization) produce very similar values of the training error E [39, 40]. Inorder to verify the observation...
... that progress has a direct bearing on signal processing. In particular, the nonlinearnature of neural networks, the ability of neural networks to learn from their environments in super-vised ... square errorsfor l=2:L, % the l-th layeru{l}=w{l}*[ones(1,K);z{l-1}]; % u{l} is n(l) by Kz{l}=actfun(u{l},atype(l));enderror=d-z{L}; % error is N by KE(t)=sum(sum(error.*error));% Error ... powerfulmodeling tools motivated numerous research efforts to explore new signal processing applicationsof artificial neural networks. During the course of the research, many neuralnetwork paradigms...
... computationalperformance and application domain for various neuralnetwork architectures. 4.2 Artificial Neural Networks Artificial neural networks have emerged in recent years as a major means for ... chapter,and the references cited, the interested reader is referred to Zhang and Huang [1995] for a review ofartificial neural networks for other, closely related areas in manufacturing.ReferencesArizono, ... nonlinear mapping between the features and the part families. The number of neuronsrequired for the middle layer is normally determined through trial and error. Neural networks are attimes criticized...
... processing information in a parallel distributed fashion. Feedforward neural networks and recur-rent neural networks are two major classes of artificial neural networks. Feedforward neural networks, ... process control. The neural network models used were multilayer feedforward networks, ART networks, and cerebellar model artic-ulation controller, as shown in Figure 2.4. Neural networks are promising ... feedforward neuralnetwork trained using the BP algorithmto control a turning process. Given feed rate error and change in the error, the trained neural network was able to recommend the control...
... block to generate the PWM outputs.-forforforforforforforforforfor(5)-forforforforforforforforforfor(6)664 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 38, NO. 3, MAY/JUNE ... two-levelinverter and, therefore, the SVM algorithm to be implemented ina neuralnetwork is considerably more complex. In the proposedscheme, a three-layer feedforward neuralnetwork receives thecommand ... Fig. 4 for all the regions in thesix sectors. For example, the phase-turn-on time expressionsin mode 1 can be derived as-forforforforforfor(3)-forforforforforfor(4)whereand...
... using a neuralnetwork model.hardenedlayerhardeninglayer depthpyrometer circuitairlaser resonatorsegmented mirror4 kw CWCO2 Laser(a) A laser surface hardening processS45Cexperimentallyobtainedhardening ... feedback error learning adopting two neural networks, one for esti-mation of the weld pool size and one for a feedback forward controller. The networks are a multilayerperceptron and the error back ... indentationArc welding Weld currentAcoustic waveTem peratureVision imageWelding current,arc voltageUltrasonic soundPerceptronPerceptronPerceptronPerceptronPerceptronPerceptronWeld pool...
... becontrolled by the neural network; the inference processing of the fuzzy system is responded to by the neural network. 15.2.2 Fuzzy NeuralNetwork In this chapter, a new neuralnetwork with ... employedindividually, there are great advantages to using them synergistically, resulting in what are generallyreferred to as neuro-fuzzy approaches [19]. Neural networks are organized in layers, each ... tool breakage and tool wear. This chapteraddresses how to apply the fuzzy neuralnetwork and wavelet transforms to TCM. First, the fuzzy neural network and the wavelet transforms are respectively...
... symmetric cryptography based on chaotic signal generator and a clipped neural network. Advances in Neural Networks-ISNN, Intl. Symp. Neural Networks Proc., Part II. Lecture Notes in Computer Science, ... overall error to test network performance. The training set is repeatedly presented to the network and the weight values are adjusted until the overall error is below a predetermined tolerance. ... reasonable outputs will result before training. The weights are adjusted to reduce the error by propagating the output error backward through the network. This process is where the backpropagation...
... between predictors (suchas meteorological and air quality variables) andpredictand (in this case, hourly concentration). Neural networks, in particular the multi-layer perceptron(Hornik et ... Press, Ann Arbor.Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedfor-ward networks are universal approximators. Neural Networks 2,359–366.Jain, A., Zongker, D., 1997. Feature selection: ... The parametersused were 25 hidden neurons, learning algorithm ofscaled conjugated gradient back-propagation, the per-formance function of regularized mean squared error(RMSE), hyperbolic...