一种用于多类模式识别的综合感知器网络和学习算法

An Integrated Perceptron Network and Learning Algorithm for Multi-Class Patterns Recognition

提出一种由感知器神经元组合成的综合网络模型及相应的学习算法 .网络由 3层感知器神经元组成 ,中间层到输出层的权值相应地为 +1或 - 1.输入层到中间层的权值通过学习获得 ,且中间层每个神经元的权值单独学习完成 .该学习算法在有限次迭代步骤内终止 .当算法终止时 ,对于可线性划分的多类模式识别问题总是能找到正确的解 .如果还有模式不能识别 ,则说明这是一个不可线性划分的多类模式识别问题 。

A new integrated perceptron network and the related learning algorithm is proposed. The network is made up of three layers, the weights of the middle layer to the output layer being +1's or -1's correspondingly. The weights of the input layer to the middle layer are obtained by learning, and the learning process of each neuron in the middle layer is completed individually. Furthermore, the algorithm is stopped within finite steps. Upon termination of the algorithm, the correct weight must be found for a linearly separable multiclass pattern recognition. If there exist some patterns that cannot be recognized, then the set of patterns is a nonlinearly separable multiclass pattern recognition problem. Simulation results of the recognition of digits show the effectiveness of the proposed network model and algorithm.