非同步的数值尖端神经膜系统具有局部同步
Hongyan Zhang1, Yuzhen Zhao1, Xiyu Liu1
1Business School, Shandong Normal University, Jinan, 25000, Shandong, P. R. China.
International journal of neural systems
|September 10, 2024
概括
这项研究引入了一种新的尖端神经P系统 (SN P系统) 与局部同步的神经元集,增强计算能力. 新系统的有效性与膜计算中的现有模型相美.
科学领域:
- 膜计算技术的使用
- 计算神经科学是一种神经科学.
- 人工神经网络的人工神经网络
背景情况:
- 尖端神经P系统 (SN P系统) 是第三代神经网络模型,自2006年以来在膜计算中得到广泛研究.
- 全球异步数值尖端神经P系统 (ANSN P系统) 提供了更广泛的适应性.
- 生物神经科学揭示了一些神经元社区同步运作.
研究的目的:
- 为了研究一个新的全球异步尖端神经P系统 (ANSN P系统),集成本地同步神经元集.
- 探索局部同步如何影响计算属性,例如减少值依赖和增强控制不确定性.
- 评估新系统作为生成器和接受器的计算能力.
主要方法:
- 设计和分析一种新的ANSN P系统,包括局部同步神经元集.
- 在生成模式下检查ADD,SUB和FIN模块.
- 在接受模式下分析INPUT和ADD模块.
主要成果:
- 新的ANSN P系统在生成和接受模式中展示了计算能力.
- 与现有的SNP系统进行比较,考虑神经元数量和每个神经元的规则,显示了可比的有效性.
- 局部同步神经元集的结合增强了控制的不确定性,并减少了值依赖.
结论:
- 拟议的ANSN P系统与局部同步神经元集是膜计算中的可行和有效模型.
- 这个模型为在异步计算框架中整合同步行为提供了一个新的视角.
- 该系统的性能至少与现有的SNP系统一样有效,为进一步研究铺平了道路.
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