The Adaptive Noise Canceller Based On Back Propagation Algorithm And Genetic Algorithm

1. IntroductionThe technology of the adaptive noise canceller is on signal processing ,which can clear off background noise effect better. When the disturbance source from environment is not available ,its way to transmit is a constant change ,background noise and tested sound wave are similar .Using the technology of the adaptive noise canceller ,we can clear off disturbance from environment effectively better and acquire high SNR of tested signal .Theoretically, the adaptive noise canceller is a kind of expansion based on the adaptive filter. In brief, we change the expected signal input of the adaptive filter into the primitive input end that a signal adds the noise and interferes. And its input end changes into an interferes noise end, we may offset primitive input noise of interfere by adjusting parameter of transversal filter, at this moment the error outputted is a useful signal. In the collection and processing of digital signal, the linear filter is a method of the most frequently used elimination noise. It is easy to analyse linear filter, because using minimum criterion of variance it can find close solving. If the type of noise is one gauss of noises, it can reach best filtering result. While gathering in the real digital signal, the noise disturbance superposing signal is not single gauss noise. But the linear filter required medium noise of wave filter is skew, it makes linear filter to non-gausses noise filtering performance drop. In order to overcome the shortcoming of linear filter, we often use non-linear filter. So in this paper the method of filter processing to signal is by neural network.2. The adaptive noise canceling based on the back propagation algorithm and genetic algorithm joinedIn this paper, the author studied the adaptive algorithm on the basis of the adaptive noise canceller principle mainly. The paper proposed a kind of new algorithm that a designed method to the adaptive noise canceller based on back propagation network optimization with genetic algorithms. The author made a summary to the BP network structure and algorithm, had analysed its main defect and reason which it produced. Since traditional BP network is a non-linear optimization question, the local minimum problem will exist unavoidably in this. The extreme value of the network , through revising along an improved a little step of direction, try hard to reach the global solutions of making the error function minimize ,but in fact we obtain the local optimal solution. In the learning process, it is slow to drop, it is slow to study the speed, it is easy to appear in a long-time smooth district of error, namely appear in the platform.Through an analysis of GA document, generalization and summary, we find that the GA compares with other search methods, The advantage of the genetic algorithm (GA) lies in: do not need differential value of goal function; run side by side and search for, it is with high efficiency to search for; it search for space entirely and spread all over, it is easy to reach the global optimal solutions. So we optimize BP neural network with GA, can make the neural network have evolving, adaptive ability .The inspiration of the structure optimization method of BP-GA is:(1) Utilize BP neural network mapping the relation of design variable and goal function, restriction;(2) Realize optimizing and searching for with the GA;(3) The calculation of fitness function in the GA adopts the neural network to realize.The design step of BP-GA mixed algorithms is:(1) Analyse the question, put forward goal function, design the variable and restraining the condition;(2) Establish the proper training sample, calculate and train the sample;(3) Train the neural network;(4) Adopt the GA to seek the optimal structure;(5) Use the trained neural network to check up the GA results, if they meet the demands, it is over to calculate; if the error does not meet the demands, will make the examine results add to the trained samples, then carry out 3-5 steps repeatedly until meeting the deman...