自组织堆叠的2型模糊神经网络与规则通用化
概括
类型-2模糊神经网络 (T2FNNs) 有效地模拟非线性系统,但面临多线性. 一种新的自组织堆叠T2FNN与规则泛化 (RG-SOST2FNN) 克服了这些问题,提高了复杂系统的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 模糊系统 (Fuzzy Systems) 是一个模糊系统.
背景情况:
- 2型模糊神经网络 (T2FNN) 是用于非线性系统建模的强大工具.
- 由于不确定性足迹 (FOU) 叠加而产生的多线性,通常会导致T2FNNs中的概括偏差.
研究的目的:
- 引入一个新的自组织堆叠T2FNN与规则概括 (RG-SOST2FNN).
- 为了提高性能,解决和减轻T2FNN中的多对线性问题.
主要方法:
- 一种使用cosine智能优先级的堆叠技术,用于T2FNN融合与稀疏,非对线输入.
- 一个动态堆叠的框架与规则集群生成规则调整和多样性.
- 一个堆叠的风险缓解算法和稀疏梯度学习用于参数优化.
主要成果:
- RG-SOST2FNN有效地减少了对线性依赖和参数估计差异.
- 拟议的方法在复杂的系统中实现了最先进的性能,即使具有高多线性.
结论:
- RG-SOST2FNN为T2FNN中的多线性提供了一个强大的解决方案.
- 这种方法显著提高了模糊神经网络的概括能力和整体性能.
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