... VOL. 38, NO. 3, MAY/JUNE 2002Fig. 7. Feedforward neural- network (1–24–12)-based space-vector PWM controller.Fig. 8. Segmentation of neuralnetwork output forU-phasePstates.and signals ... backpropagation technique inthe MATLAB-based NeuralNetwork Toolbox [8] was usedfor offline training. The network was simulated with anopen-loop volts/Hz-controlled induction motor drive and eval-uated ... the neuralnetwork topology with theperipheral circuits to generate the PWM waves. It consists of a1–24–12 network with sigmoidal activation function for middleand output layers. The network...
... based monitoring and control schemes. (a) A neural identifier combined withan adaptive controller. (b) A gain-tuning neuralnetwork controller. (c) A feedforward neural controller combinedwith ... the neuralnetwork controller combined with a simple conventionalcontroller, shown in Figure 12.10(c). In this control structure, the conventional controller is used toprovide the network controller ... feedback controller. (d) A neural controller combined with a neural identifier.adaptationmechanismcontrolleruprocessNMidentifieryd(a) A neural identifier combined with an adaptive controller+_yyconventionalcontrollerprocessNNyu(b)...
... the neuralnetwork 2.2 NeuralNetwork In this subsection, text extraction from static image or video sequences is accomplished using the back-propagation (BP) algorithm on a neural network. ... 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, new input data will ... 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 is simpler...
... 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 the spatial characteristics but ... width and height, there are totally 14 features used for the neuralnetwork analysis. D. Contours classification using neuralnetwork We extract the above features which are helpful for classification ... Backpropagation neuralnetwork can handle any nonlinear relationship after training including the complicated inter-relationship between the features. Making use of neural networks will also...
... gọn như sau :Mạng nhiều lớp neuronCHƯƠNG 2MÔ HÌNH MẠNG NEURAL NETWORKSMô hình mạng Neural tổng quát có dạng như sau :Ngày nay mạng Neural có thể giải quyết nhiều vấn đề phức tạp đối với con ... định dạng của dữ liệu vào ảnh hưởng đến việc mô phỏng của mạng. Có hai loại mạngstatic network và dynamic network. Hai kiểu vector đầu vào cơ bản là kiểu xảy ra đồng thời(concurrently) và kiểu ... trong lớpa : vector ngõ ra của lớp neuronHàm truyềnCó rất nhiều hàm truyền áp dụng trong Neural Networks, trong đó ba hàm thường sử dụng nhấtlà Hard Limit, Linear, Log-Sigmoid.Tổng quát...
... method for neural networks,in Neural Networks for Speeach and Image Processing,R.J. Mammone, Ed., Chapman & Hall, BocaRaton, FL, 1993.[10] A. Krogh and J. Vedelsby, Neural networks ensembles, ... three complementary parts: neuralnetwork fundamentals, neural network solutions to statistical signal processing problems, and signal processing applications using neural networks. In the first part, ... (LVQ) neural network. The above discussion is summarized in Table 1.5.TABLE 1.5 Pattern Classification Methods and Corresponding Neural Network ImplementationsPattern Classification Methods Neural...
... a neuralnetwork with cyclic topology contains at least one cycle formed by directedarcs. Such a neuralnetwork is also known as a recurrent network. Due to the feedback loop,a recurrentnetwork ... Artificial NeuralNetwork (ANN) Models — An Overview1.2.1 Basic NeuralNetwork ComponentsA neuralnetwork is a general mathematical computing paradigm that models the operations of bio-logical neural ... handbook — neural networks for signalprocessing. The chapter first discusses the definition of a neuralnetwork for signal processingand why it is important. It then surveys several modern neural network...
... 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 ... 4.2 Artificial Neural Networks 4.3 A Taxonomy of NeuralNetwork Applicationfor GT/CM 4.4 Conclusions 4.1 Introduction Recognizing the potential of artificial neural networks (ANNs) ... Deterministic neuralnetwork models do not have the capabilityto escape from local optimal solution. Stochastic neuralnetwork models attempt to avoid local optimalsolutions. Stochastic neural network...
... types of neural networks included ART networks, Hopfield networks, and SOM neural networks. Weaknesses of neural networks for modeling and design of manufacturing systems result from neural networks ... patterns of control chart. The trained neuralnetwork can identify theshift, trend, and cycle patterns in the control chart by taking 16 consecutive points from the control chart. The neuralnetwork ... feedforward neural networks. Recurrent neural networks, such as the Hopfield networks, are usually used as computational models forsolving computationally intensive problems. Typical examples of recurrent...
... can 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 ... neuralnetwork is presented to describe the relationshipbetween the tool wear conditions and the monitoring features. 15.2 Fuzzy NeuralNetwork 15.2.1 Combination of Fuzzy System and Neural ... µ ∈ [0,1]. To test the fuzzy neuralnetwork (FNN), it is compared with the BP neural networks (BPNN) [22].Under the same conditions (training sample, networks structure (5 × 5), learning...