... 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...
...
totally 14 features used for the neuralnetwork analysis.
D. Contours classification using neuralnetwork
We extract the above features which are helpful for
classification of text and non-text ...
Figure 8. 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 ...
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 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...
... a neuralnetwork with cyclic topology contains at least one cycle formed by directed
arcs. Such a neuralnetwork is also known as a recurrent network. Due to the feedback loop,
a recurrentnetwork ... 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 ... 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 ... feedforward neural networks.
Recurrent neural networks, such as the Hopfield networks, are usually used as computational models for
solving computationally intensive problems. Typical examples of recurrent...
... 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...
... the neural network. The model in Figure 15.2(b) shows that the fuzzy system can be
controlled by the neural network; the inference processing of the fuzzy system is responded to by the
neural network.
... neuralnetwork is presented to describe the relationship
between 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...
... 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, ... Cheung and Z. Wang, 2004.
A chaotic -neural- network- based encryption
algorithm for JPEG2000 encoded images.
Advances in Neural Networks, Intl. Symp. Neural
Networks Proc., Part II, Lecture Notes ... artificial neuralnetwork outputs and
decryption signals respectively.
Fig. 5: Encrypted signal
Fig. 6: Neuralnetwork output
Fig. 7: Decrypted signal
CONCLUSION
As the computer network...
... Geophysical
Research 90 (C5), 8995–9005.
Yao, X., 1999. Evolving artificial neural networks. Proceedings of the
IEEE Transactions on Neural Networks 87 (9), 1423–1447.
ARTICLE IN PRESS
H. Niska et al. / Engineering ... values is increased, enhancing the error
term by using some regularization technique and
recurrent neural networks, where the temporal patterns
are better considered.
Acknowledgements
This research ... 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: evaluation,...