The Bionic Neural Network Algorithm Under The Framework Of AdaBoost Algorithm

Bionic neural network experiences ten years of development. Researchers explore it in many fields of pattern recognition. but the performance still needs to be improved. This paper introduces the knowledge biomimetic pattern recognition algorithms and Boosting algorithm, then the author proposes an improved biomimetic pattern recognition algorithm. The work of this paper is as follow: firstly the author designs a new method of construction bionic neural networks by learning the AdaBoost algorithm of boost learning. In this method, every bionic neuron is looked as a weak classifier. Each weak classifier through feedback to determine the weight of this bionic neuron in the newly constructed bionic neural network. In this way we can construct a bionic neural network focuses more attention on the sample which has a larger impact of those results and improve the bionic neural network classification capabilities; secondly, in a single bionic neuron structure, take into account the influence of the positional relationship between the pixels of the image space, the author uses the Image Euclidean distance(IMED) which has a better performance than traditional Euclidean distance. This new application of IMED can enhance the performance of individual bionic neuron; thirdly the author simplifies the IMED as a convolution operator in some certain circumstances to reduce the amount of computation. The bionic neuron constructed by this operator improves the classification when it is used as a weak classifier.Finally, the algorithm is tested on a variety of commonly used data sets. Conclusion: The improved bionic neural network algorithm proposed by this paper shows better performance in proven experiments than the traditional algorithm.