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The Improved Artificial Neural Network Algorithm and It’s Image Compression

Author: LiuXiangYang
Tutor: WangRuYun
School: Hohai University
Course: Applied Mathematics
Keywords: Artificial Neural Networks Relative error Cumulative error BP algorithm Adaptive Image Compression
CLC: TP391.41
Type: Master's thesis
Year: 2003
Downloads: 241
Quote: 2
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Taking into account In many cases, people are more concerned with the relative error of the forecast value and the actual value of the prediction model , this paper, the actual output and the desired output relative error sum of squares as the objective function , given a based on relative error sum of squares and for a minimum of BP algorithm . Taking into account the actual network output values ??ranging between 0 and 1 , the ideal output value for the practical problems are given a standardized approach . Cases test confirmed by a large number of operators in the fitted values ??or predictions based on the square of the relative error and for the inspection standards under the premise , the algorithm obtained better than the traditional BP algorithm, the results based on the absolute error sum of squares as the objective function . Final learning outcomes through the accumulated error to the evaluation of artificial neural network , which aimed directly at the accumulated error to study the multi-layer artificial neural network algorithm to learn quickly . We first briefly trapezoidal descent method based on the cumulative error , on this basis , an adaptive learning rate adjustment programs . After a large number of examples inspection , under the same accuracy requirements , the convergence speed of the proposed algorithm greatly accelerate and effectively overcome the general descent method based on the accumulated error ladder with shock in the learning process . Image compression based on the error back propagation algorithm work for many , but there is the problem of the artificial neural network training time is longer, low precision . Taking into account the use of three and more than three BP network for image compression , the effective information is the connection weights between the output value of the intermediate layer unit and the intermediate layer and output layer , the connection weights of the input layer and the middle layer is redundant that have a negative impact on learning speed and compression quality . Based on this , we propose a new two-story back propagation network topology and algorithms , in order to further improve the compression ratio of the image compression and compression quality , we propose a new three-layer back propagation network topology and algorithms . The machine through the compression test , relative to the three-layer BP network , more than three BP network and nested BP network image compression compression ratio , the learning speed and compression quality has greatly improved , and achieved good results .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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