The Improved Artificial Neural Network Algorithm And It's Image Compression

As in many cases, people pay more attention to the relative error between actual output values and the idea output values, in this paper, an improved BP algorithm based on the smallest square sum of the relative error is proposed, which looks on the square sum of relative error between the idea output and the actual output as the object function. Because the network's actual output values are between 0 and 1, a method of standardization management is given to the idea output of actual problem in this paper. It has been proved in many examples that the BP algorithm based in the square sum of the relative error is better than the conventional BP method.Since we value the learning effect of neural networks by cumulative error, the paper pay direct attention to it to study the BP algorithm. First, we introduce the trapezoid drop method based on cumulative error, and give a study way of adaptation. It has been proved in many examples that in the same precision need our algorithm get a good result in the convergence speed, and has effectively solved the convergence shake of algorithm study in generic trapezoid drop method.There has been many techniques of image compression based upon back propagationarithmetic, but they all have their own limits, the long study speed and lower compression quality. In the process of image compression, Considering that the three or more layers BP networks have some redundancies in the weights between input layer and meddle layer so as to effect the network's study speed and compression quality, we bring forward a new two layers back propagation networks and it's arithmetic. To get the much more quality and rate of image compression, we bring forward another new three layers back propagation networks and it's arithmetic. It has been proved in many examples that the new networks get a good result in the compression rate, study speed and compression quality of image compression than other back propagations.