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摘要: 二进制数据表示具有简洁高效的特点,随机噪声有助于系统摆脱局部极小.新型的随 机神经网络模型采用随机加权联接,内部数据表示为随机二进制序列形式,实现十分高效.文中 分别就前馈型网络和反馈型网络进行了深入的讨论,给出了前馈型网络的梯度下降学习算法, 为反馈型网络设计了快速有效的模拟退火算法和渐进式Boltzmann学习算法.通过对PARITY 问题的测试,发现了新模型的一些有趣特征,实验结果表明梯度下降学习效果显著.利用渐进式 Boltzmann学习,反馈型网络被成功地用于带噪声人脸识别.
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关键词:
- 随机计算 /
- 随机二元神经网络 /
- 平稳分布 /
- 模拟退火 /
- 渐近式Boltzmann学习
Abstract: The highly efficient binary representation scheme has dominated the world of computation for a long time. However, the deterministic binary representation seems to be challenged in stochastic neural computation which makes use of the random noise to escape from local minima. This paper presents a novel stochastic binary neural network which uses stochastic weights and the 'Stochastic binary sequence' data representation. It is very attractive with its great potential to be implemented efficiently. Both the feedforward and the recurrent stochastic binary networks have been discussed in depth. The gradient descent learning techniques are described for the feedforward network. A novel simulated annealing method and an incremental Boltzmann learning algorithm have been proposed. Simulation results on the PARITY problem and a face recognition task show the excellent performance of the model.
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