... 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...
...
DESIGN ARTIFICIALNEURALNETWORK MODEL
Neural networks are computer models that mimic the knowledge
acquisition and organization skills of the human brain. Since, the
characteristics of a neural ... GW (1992). " ;Neural network modeling of the mechanical behavior
of sand," Proc. 9
th
Conf. ASCE, New York, pp 421-424.
Garson, GD (1991). "Interpreting neural- network connection ... In this study, a back-propagation
neural network model for estimating of proper strain rate form soil
parameter is proposed. The back-propagation neuralnetwork program
adopted in the present...
... gọn như sau :
Mạng nhiều lớp neuron
CHƯƠNG 2
MÔ HÌNH MẠNG NEURAL NETWORKS
Mô 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 mạng
static network và dynamic network. Hai kiểu vector đầu vào cơ bản là kiểu xảy ra đồng thời
(concurrently) và kiểu ... trong lớp
a : vector ngõ ra của lớp neuron
Hàm truyền
Có rất nhiều hàm truyền áp dụng trong Neural Networks, trong đó ba hàm thường sử dụng nhất
là 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, Boca
Raton, 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 Implementations
Pattern Classification Methods Neural...
... Artificial NeuralNetwork (ANN) Models — An Overview
1.2.1 Basic NeuralNetwork Components
A neuralnetwork is a general mathematical computing paradigm that models the operations of bio-
logical neural ... for integrating neural networks with other
signal processing algorithms. Another important issue is how to evaluate neuralnetwork paradigms,
learning algorithms, and neuralnetwork structures ... handbook — neural networks for signal
processing. The chapter first discusses the definition of a neuralnetwork for signal processing
and why it is important. It then surveys several modern neural network...
... computational
performance and application domain for various neuralnetwork architectures.
4.2 Artificial Neural Networks
Artificial neural networks have emerged in recent years as a major means ... CRC Press LLC
4
Neural Network
Applications for
Group Technology
and Cellular
Manufacturing
4.1 Introduction
4.2 Artificial Neural Networks
4.3 A Taxonomy of NeuralNetwork Application
for ... Deterministic neuralnetwork models do not have the capability
to escape from local optimal solution. Stochastic neuralnetwork models attempt to avoid local optimal
solutions. 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 ... parallel distributed fashion. Feedforward neural networks and recur-
rent neural networks are two major classes of artificial neural networks. Feedforward neural networks,
Jun Wang
The Chinese University
of ... The neuralnetwork models used were multilayer feedforward networks, MAXNET,
Hopfield networks, ART networks, and stochastic networks. The knowledge acquisition capabilities of
neural networks made...
... the neuralnetwork topology with the
peripheral circuits to generate the PWM waves. It consists of a
1–24–12 network with sigmoidal activation function for middle
and output layers. The network ... VOL. 38, NO. 3, MAY/JUNE 2002
Fig. 7. Feedforward neural- network (1–24–12)-based space-vector PWM controller.
Fig. 8. Segmentation of neuralnetwork output for
U
-phase
P
states.
and signals ... therefore, the SVM algorithm to be implemented in
a neuralnetwork is considerably more complex. In the proposed
scheme, a three-layer feedforward neuralnetwork receives the
command voltage and angle...