A study on DBM network with non-monotonic neurons

In this paper, we report a study on learning ability of a deterministic Boltzmann machine (DBM) with neurons which have a nonmonotonic activation function. We use an end-cut-off-type function with a threshold parameter `θ´ as the nonmonotonic function. Numerical simulations of learning nonlinear problems, such as the XOR problem and the ADD problem, show that the DBM network with nonmonotonic neurons has higher learning ability compared to the network with monotonic neurons, and that the nonmonotonic neural network has novel effects which adjust the number of neurons. We have designed an integrated circuit of the 2-3-1 DBM network. The use of the nonmonotonic neurons make it possible to integrate a large scale neural network because of the simple circuit design