A Simple Class of Binary Neural Networks and Logical Synthesis

作者：Nakayama Yuta^*; Ito Ryo; Saito Toshimichi

来源：IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2011, E94A(9): 1856-1859.

DOI：10.1587/transfun.E94.A.1856

• binary neural networks
• genetic algorithms
• logical synthesis

This letter studies learning of the binary neural network and its relation to the logical synthesis. The network has the signum activation function and can approximate a desired Boolean function if parameters are selected suitably. In a parameter subspace the network is equivalent to the disjoint canonical form of the Boolean functions. Outside of the subspace, the network can have simpler structure than the canonical form where the simplicity is measured by the number of hidden neurons. In order to realize effective parameter setting, we present a learning algorithm based on the genetic algorithm. The algorithm uses the teacher signals as the initial kernel and tolerates a level of learning error. Performing basic numerical experiments, the algorithm efficiency is confirmed.

• 出版日期2011-9