Research Initiation Award: Comparisons of Deterministic/ Stochastic Neural Network Computing for Solving NP-Complete Problems

本研究的重点是研究一种基于模拟退火法和不带模拟退火法的神经网络高并行分布式处理系统。所研究的神经网络利用了柯西机器分散控制的爬山能力和Hopfield网络的快速收敛特性。能量代价函数的重新表达和神经网络的适当权值对于减少局部极小是至关重要的。许多用于解决NP-完全问题的神经网络都采用梯度下降算法。虽然收敛速度很快，但系统很容易陷入局部极小值。采用模拟退火法的神经网络可以得到更好的结果，但收敛速度很慢。该高斯机由Sigmoid神经元和随机突触连接组成，利用了Hopfield网络的快速收敛特性和Boltzmann机器的爬山特性。这样的研究将进一步加深我们在将神经网络映射到VLSI方面的知识，并导致更有效的学习算法。因此，强烈建议提供支持。

This research focuses on studying a highly parallel distributed processing system based on neural networks with and without simulated annealing. The neural network under investigation takes advantage of the hill-climbing capability of Cauchy machines with decentralized control and the fast convergence property of Hopfield networks. A reformulation of an energy cost function and appropriate weights of the neural network are crucial to reducing local minima. Many neural networks used in solving NP-complete problems apply a gradient descent algorithm. Although convergence is fast, it is very easy for the system to be trapped in a local minimum. Neural networks employing simulated annealing give better results but converge very slowly. The proposed Gaussian machine, which is composed of sigmoid neurons and stochastic synaptic links, takes advantage of the fast convergence property of the Hopfield network and the hill-climbing property of the Boltzmann machine. Such a study will further our knowledge in mapping neural networks to VLSI and result in more efficient learning algorithms. Support is, therefore, highly recommended.