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个高度并行的分布式 基于神经网络的模拟和非模拟处理系统 退火 研究中的神经网络利用了 分散柯西机的爬山能力 控制和Hopfield网络的快速收敛性。 一 重新制定能量成本函数和适当的权重 神经网络对于减少局部极小值至关重要。 许多用于解决NP完全问题的神经网络都采用了 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。 神经网络 采用模拟退火得到更好的结果，但收敛非常 慢慢 建议的高斯机，这是由sigmoid 神经元和随机突触链接，利用快速 Hopfield网络和爬山法的收敛性 玻尔兹曼机的性质。 这项研究将进一步促进我们的 将神经网络映射到超大规模集成电路的知识并带来更多成果 高效的学习算法 因此，支持度很高。 推荐

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.