一种粒子群优化原型模式修正力度的协同分类方法

协同模式识别是一种有着抗噪声、抗缺损、强鲁棒性等诸多优良特性的模式识别方法,其中原型模式的选取模式识别结果有着决定性的作用,其选取直接决定着模式识别的结果和效果,各种方法中信息反馈修正的方法能获得较的效果,但易出现信息饱和的问题;提出了一种粒子群优化修正力度的处理机制,能有效改善此问题,获得最优原型;改进的算法应用于纹理和鼻咽癌细胞图像识别,结果表明,该方法能有效地提高协同神经网络的识别率和可靠性,且识速度也有提高.

A Synergetic classification algorithm based on prototype modify with particle swarm optimization measure

The synergetic pattern recognition is a new way of pattern recognition with many excellent features such as noise resistance, deformity resistance, and better robustness,the selection of prototype patterns is very important to pattern recognition of synergetic approach, which set the tone for the recognition performance of synergetic approach, the superposition modify of information is better in the existing methods of prototype selection, prototype modify method with particle swarm optimization measure is applied to avoided information saturation,and get the optimal prototype, experiment result on Brodatz texture images and nasopharyngeal carcinoma cell images shows that the new algorithm can effectively search the optimal prototype patterns,the synergetic recognition method proposed in this paper is more available than classical synergetic pattern recognition method, and excellent, correct and fast recognition result has been achieved, with good potential clinical application.