Detection of Weak Signals Based on RBF Neural Network Filtering

2011 ◽  
Vol 211-212 ◽  
pp. 846-849
Author(s):  
Jian Jun Li
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Adaptive Neural Network ◽  
Self Study ◽  
Characteristic Signal ◽  
Adaptive Noise ◽  
Band Signal ◽  
Detection Of Weak Signals

The algorithm is presented in this paper based on the character about RBF adaptive neural network filtering needn’t previous information of input single and noise and has better ability of nonlinear mapping and self-study. The adaptive noise cancellation system is designed. The system can improve LMS algorithm slow convergence speed and extraction of narrow band signal faults and has small amount of calculation and real-time good characteristic. The effect is better at Using this system in the field of life characteristic signal detection identification. Results show that the system has the high feasibility and validity.

scholarly journals Adaptive Noise Cancellation for speech Employing Fuzzy and Neural Network

2011 ◽  
Vol 7 (2) ◽  
pp. 94-101
Author(s):  
Mohammed Miry ◽  
Ali Miry ◽  
Hussain Khleaf
Keyword(s):  
Neural Network ◽  
Adaptive Filtering ◽  
Communication Systems ◽  
Digital Signal ◽  
Noise Cancellation ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Long Distance ◽  
Wide Range ◽  
Adaptive Noise

Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to-Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.

Study on Anti-Noise Materials with Multi-Level Filter Based on the Adaptive Noise Cancellation System

2014 ◽  
Vol 886 ◽  
pp. 390-393
Author(s):  
Jing Mo ◽  
Wei He ◽  
Dan Su ◽  
Jing Wei Wu
Keyword(s):  
Noise Cancellation ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Filter Performance ◽  
Multi Level ◽  
Adaptive Noise ◽  
Filtering Effect

It presents the Multi-level filters idea of the adaptive noise cancellation system based on the fact that the adaptive noise cancellation system cant filter out noise signal completely. According to the linear combination and the variable step-size LMS algorithm, it analyzes the effects of the two level filters. Theory analyzing and simulation results prove that the multi-level filter can get a better the filtering effect than the one-filter, which improves the filter performance in terms of the fast convergence speed, tracking speed and the low maladjustment error. And the anti-noise materials with multi-level filter based on the adaptive noise cancellation system has the good de-noising ability of noisy signals.

Research and Design of Adaptive Noise Cancellation Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1942-1945
Author(s):  
Jie Zhang ◽  
Shi Qi Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Noise Cancellation ◽  
Biological Species ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Swarm Optimization ◽  
Adaptive Noise

Particle swarm optimization (PSO) is a kind of evolutionary computation technology which simulates the behavior of biological species. The essence of adaptive noise cancellation (ANC) is adjust the weight value of filter based on the input signals, the LMS algorithm is commonly used in this system, However, the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size μ, and the optimum value of μ cannot be determined easily, In this paper, Particle Swarm Optimization with linear decreasing inertia weight is proposed to solve the filter problem instead of LMS, taking the FIR filter of ANC as example, the simulation shows that ANC based on the PSO algorithm is better than classic ANC based on the LMS algorithm, and it gives the satisfactory results.

An Improved Adaptive Neural Network Detecting Approach to Harmonic Currents with Changeable Learning Parameters

2012 ◽  
Vol 433-440 ◽  
pp. 6546-6550
Author(s):  
Jun Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Simulation ◽  
Active Power ◽  
Adaptive Neural Network ◽  
Active Power Filters ◽  
Adaptive Weights ◽  
Power Filters ◽  
Harmonic Currents ◽  
Adaptive Noise

Using the adaptive noise canceling technology, this paper proposes a new detecting approach to harmonics and reactive currents based on neural networks with changeable learning parameters. The structure of this neural network and the adaptive weights adjusting algorithm are presented. The contradiction of the detecting speed and the precision has been settled preferably. The proposed detecting approach can be used for detecting the harmonics and the reactive currents of active power filters. The results of the theoretical analysis and computer simulation confirm the validity of the approach.

The Design of Adaptive Noise Cancellation Filter Based on DSP Chip

2014 ◽  
Vol 971-973 ◽  
pp. 1786-1790
Author(s):  
Xiu Min Wang ◽  
Ting Ting Li ◽  
Liang Shan
Keyword(s):  
Digital Filters ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Speech Communication ◽  
Adaptive Noise Cancellation ◽  
Change Characteristics ◽  
The Common ◽  
Adaptive Noise

The speech signal usually could not be extracted correctly from the digital speech communication system with strong interference. As for this kind of system, the common fixed coefficient digital filters (FIR, IIR) are unable to achieve the best effect of filtering. Whereas the adaptive filter could extract the available signals properly by adjusting the filter coefficient automatically without knowing the change characteristics of the noise signal. In this paper, we designed an adaptive noise cancellation filter based on LMS algorithm on the DSP chip and verification of the filter was done on the TMS320C5509 platform. The results show that the adaptive noise cancellation designed in this paper could extract the available signals properly and improve the quality of the speech communication.

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