Study on Fuzzy Neural Network Classifier Blind Equalization Algorithm

Sun, Yu; Zhang, Liyi; Liu, Ting; Li, Yanqin

doi:10.1109/icia.2006.305792

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…Fuzzy neural network model consists of k fuzzy rules, whose form is expressed as In this paper, the four-layer structure of fuzzy neural network is shown in Fig.2 [5],…”

Section: Fuzzy Neural Network Classifiermentioning

confidence: 99%

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Application of blind equalization algorithm based on fuzzy neural network in QAM

Sun

Zhang

Duan

et al. 2010

2010 International Conference on Intelligent Control and Information Processing

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A blind equalization algorithm based on fuzzy neural network was proposed. Blind channel estimation and fuzzy neural network classifier were utilized to realize blind equalization. Firstly Blind channel estimation was used to identify the character of the channel. Signals were rebuilt by de-convolution, and the original judgment equipment was replaced by fuzzy neural network classifier. Simulations indicate that the novel algorithm improves convergence and bit error rate and so on.

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“…Fuzzy neural network model consists of k fuzzy rules, whose form is expressed as In this paper, the four-layer structure of fuzzy neural network is shown in Fig.2 [5],…”

Section: Fuzzy Neural Network Classifiermentioning

confidence: 99%

“…Then, the output signals were classified by FNN. Fig.3 is the block diagram of blind equalization based on fuzzy neural network [5].…”

Section: Fuzzy Neural Network Classifiermentioning

confidence: 99%

Application of blind equalization algorithm based on fuzzy neural network in QAM

Sun

Zhang

Duan

et al. 2010

2010 International Conference on Intelligent Control and Information Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In case of its superiority, it draws more and more attention [1][2][3]. Equalization algorithm with a nonlinear mapping can be described as a divided problem in the decision region of view space, whereas neural networks can form a relatively complex nonlinear decision surface, thus they are widely used for channel equalization algorithm [4][5][6]. In neural networks, Radial Basis Function(RBF) neural network with a simple network structure and fast convergence speed attracts the attention of many researchers.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network Blind Equalization Algorithm Based on Signal Transformation

Guo

Liu

2010

AMM

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To recover QAM signals at the receiver of blind equalizer, a Fuzzy C-means clustering Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-FNN-BEA) is proposed. The proposed algorithm uses signal transformation method to debase the computational complexity of equalizer input signals and speed up the convergence rate, and makes use of fuzzy c-means clustering algorithm dividing the equalizer input signals into each cluster center with different membership values to improve the equalization performance. The proposed ST-FNN-BEA outperforms Neural Network Blind Equalization Algorithm (NN-BEA) and Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-NN-BEA) in improving convergence rates and reducing mean square error. The performance of ST-FNN-BEA is proved by the computer simulation with underwater acoustic channels.

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Section: Theory Of Bp Neural Networkmentioning

confidence: 99%

“…There is no interaction between neurons. The change of values has an inherited effect, which is repeatedly recycled until reaching the desired error, and training a matrix that is in line with the expected rate of specific gravity [3] .In Figure 1, As shown in Figure 1, the reverse-transmission neural network is essentially a nonlinear function; the independent variable is an input value of the network; the dependent variable is an output value of the network, thereby building a function relation from the dimension (n) to the dimension (m). The training network can make data become standard and network more intelligent.…”

mentioning

confidence: 99%

Image Restoration Technology Based on Discrete Neural network

Zhou

2015

MATEC Web of Conferences

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PREFACEIn human life and production activities, the majority of information acquired is visual information, such as the plate number, auto logo or model required to be identified in daily traffic. However, the role of nature often leads to incomplete information. For example, the note fonts might fade after several years, thus resulting in difficulty in obtaining commercial evidence or other activities. How to restore the incomplete information becomes a problem to be solved by modern human beings. Since the advent of the neural network, it is widely used and promoted to the image restoration technology. This paper implements the theory based on the classical mathematical theory, applies the discrete neural network in the field of image restoration, and compares the identification effect of the feedforward network. MODELING Theory of BP neural networkBP neural network refers to a kind of multilayer feedforward bionic algorithm. This algorithm has two main features: The first feature is the forward transmission of information; the second feature is the reverse transmission of error. There is no interaction between neurons. The change of values has an inherited effect, which is repeatedly recycled until reaching the desired error, and training a matrix that is in line with the expected rate of specific gravity [3] .In Figure 1, As shown in Figure 1, the reverse-transmission neural network is essentially a nonlinear function; the independent variable is an input value of the network; the dependent variable is an output value of the network, thereby building a function relation from the dimension (n) to the dimension (m). The training network can make data become standard and network more intelligent. The training steps are as follows:First step: initialize the network. Determine the number of nodes at the typing layer (n), the number of nodes at the hidden layer (l) and the number of nodes at the printing layer (m) according to the typing and printing matrix(X,Y). Initialize the specific gravity connected between the neurons at the typing layer and the printing layer ( ij Z and jk Z ), the range at the hidden layer (a), the range at the printing layer (b) and a given acquisition rate and agitation function. Second step: output at the hidden layer. Determine the number of nodes at the typing layer (n), the specific gravity connected between the hidden layers ( ij Z ) and the range (a) according to the matrix(X,Y), so as to calculate the output at the hidden layer (H). Image Restoration Technology Based on Discrete Neural network Duoying ZhouCollege of Computer and Control Engineering, Nankai University, Tianjin, China ABSTRACT: With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration proc...

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Study on Fuzzy Neural Network Classifier Blind Equalization Algorithm

Abstract: As a key technology in the digital communication system, blind equalization algorithm based on fuzzy neural II.

Cited by 7 publications

References 3 publications

Application of blind equalization algorithm based on fuzzy neural network in QAM

Application of blind equalization algorithm based on fuzzy neural network in QAM

Fuzzy Neural Network Blind Equalization Algorithm Based on Signal Transformation

Image Restoration Technology Based on Discrete Neural network

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