Niittylahti J
Nokia Consumer Electronics, Bochum Germany. mittylahti@research..nokia.fi
Int J Neural Syst. 1996 Mar;7(1):45-52. doi: 10.1142/s0129065796000051.
Boolean Neural Network is a neural network that operates with binary weight values of "1" and "0". Otherwise it is formally analogous to the Multilayer Perceptron (MLP). Simulated Annealing is a stochastic optimization methods that is suitable for performing nonlinear multivariable optimization tasks. Training a Boolean Neural Network is a well-suited problem to this algorithm. However, the Simulated Annealing method is computationally heavy, which makes the training procedure slow. The training speed can be improved by using custom designed hardware for the whole system including the optimization method and the neural network. Hardware prototypes of a Boolean Neural Network and the Simulated Annealing optimization method have been designed using discrete components. The Boolean Neural Network implementation is basically a dynamically configurable feedforward network of Boolean logic gates of two inputs. The Simulated Annealing implementation is a general purpose hardware tool for multivariable optimization tasks. Here it is applied to do supervised training of the Boolean Neural Network hardware.
布尔神经网络是一种使用“1”和“0”的二进制权重值进行运算的神经网络。否则，它在形式上类似于多层感知器（MLP）。模拟退火是一种适用于执行非线性多变量优化任务的随机优化方法。训练布尔神经网络是该算法非常适合的问题。然而，模拟退火方法计算量很大，这使得训练过程缓慢。通过为包括优化方法和神经网络在内的整个系统使用定制设计的硬件，可以提高训练速度。布尔神经网络和模拟退火优化方法的硬件原型已使用分立元件进行设计。布尔神经网络的实现基本上是一个具有两个输入的布尔逻辑门的动态可配置前馈网络。模拟退火的实现是一种用于多变量优化任务的通用硬件工具。在此，它被应用于对布尔神经网络硬件进行监督训练。
