一个新的二进前向多层网学习算法及布尔函数优化实现

本文首先给出二进前向多层网几何学习算法的一个改进策略，提高了原算法的学习效率，然后同个新的神经网络启发式遗传几何学习算法。ＨＧＧＬ算法采用面向知识的交叉算子和变异算子对几何超平面进行优化的划分，同时确定隐层神经元的个数及连接权系数和阈值，对任意布尔函数，ＨＧＧＬ算法可获得迄今为止隐节点数量少的神经网络结构。

A New Learning Algorithm of Binary Neural Network Used for Optimum Design of Boolean Function

A modification to the geometrical learning algorithm of binary neural network, which tries to enhance efficiency of the algorithm, is demonstrated. Then a new Heuristic Genetic Geometrical Learning algorithm(called HGGL algorithm) of the neural network used for arbitrary Boolean function approximation is presented. The algorithm imtroducesknowledge based crossover operator and mutation operator to optimally divede geometrical hypercube and evaluate the numberof the hidden netirons, connection weight and threshold. For arbitrary Boolean function, the neural network trained by HGGLalgorithm has the fewest number of hidden layer neurons comparde with existed leaning algorithms.