用于矢量量化的神经网络竞争学习算法

对典型的竞争学习算法进行了研究和分析，提出了一种基于神经元获胜概率的概率敏感竞争虎法。与传统竞争学习算法只有一个神经元获胜而得到学习不同，ＰＳＣＬ算法按照各种凶的获胜概率并通过对失真距离的调整使每个神经元均得到不同的学习，可以有效地克服神经元欠利用问题。

Neural Network Competitive Learning Algorithms for Vector Quantization

A fast VQ image coding method based on humans visual attribution and applying wavelet tree structure is proposed in this paper, naming tree structure fast VQ coding. After the characteristics of VQ was analyzed, a statistic method generating a codebook was designed, and a fast VQ coding method was represented. This method can efficiently remove correlation in image data, obtaining a low transmission bit stream. The apparent advantage of the method is to estab lish statistics codebooks for various image data, and each treating need not generate codebook s with a high coding efficient achieved, while it can apply to any opportunity with wavelet transformation used in various image and signal data compression. The experimental results show: The fast VQ coding method proposed in this paper can achieve a compression ratio of 40 with a PSNR of 36.21dB,while its total performance is superior to other methods, and a real time compression using this method may be implemented.