Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The a...Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability toprocess both numerical and granular data, leading to improved interpretability. This paper proposes a novel designmethod for constructing GNNs, drawing inspiration from existing interval-valued neural networks built uponNNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzynumbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizesa uniform distribution of information granularity to granulate connections with unknown parameters, resultingin independent GNN structures. To quantify the granularity output of the network, the product of two commonperformance indices is adopted: The coverage of numerical data and the specificity of information granules.Optimizing this combined performance index helps determine the optimal parameters for the network. Finally,the paper presents the complete model construction and validates its feasibility through experiments on datasetsfrom the UCIMachine Learning Repository. The results demonstrate the proposed algorithm’s effectiveness andpromising performance.展开更多
形式概念分析理论已经广泛地应用于计算机诸多领域.当前,模糊概念格直接构造仍然是该领域主要问题之一,其构造过程具有指数级时间复杂度.为了提高模糊概念格构造效率,文中对串行模糊概念构造算法进行并行化改进,将模糊集合组合搜索空间...形式概念分析理论已经广泛地应用于计算机诸多领域.当前,模糊概念格直接构造仍然是该领域主要问题之一,其构造过程具有指数级时间复杂度.为了提高模糊概念格构造效率,文中对串行模糊概念构造算法进行并行化改进,将模糊集合组合搜索空间映射为自然数区间,简化了搜索空间表示、划分和遍历过程,进而提出并行模糊概念构造算法(Parallel Fuzzy Next Closure,ParaFuNeC).该算法对搜索空间均匀划分,子搜索空间彼此独立,从而避免并行任务之间同步、通讯等时间耗费,达到提高模糊概念构造效率的目标.时间复杂度分析和实验结果表明该算法在大规模计算任务情况下,加速比随着并行度的提高呈正比增长趋势.另外,串行比例指标表明ParaFuNeC算法在大规模计算任务情况下具有更好的可扩展性.展开更多
基金the National Key R&D Program of China under Grant 2018YFB1700104.
文摘Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability toprocess both numerical and granular data, leading to improved interpretability. This paper proposes a novel designmethod for constructing GNNs, drawing inspiration from existing interval-valued neural networks built uponNNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzynumbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizesa uniform distribution of information granularity to granulate connections with unknown parameters, resultingin independent GNN structures. To quantify the granularity output of the network, the product of two commonperformance indices is adopted: The coverage of numerical data and the specificity of information granules.Optimizing this combined performance index helps determine the optimal parameters for the network. Finally,the paper presents the complete model construction and validates its feasibility through experiments on datasetsfrom the UCIMachine Learning Repository. The results demonstrate the proposed algorithm’s effectiveness andpromising performance.
文摘形式概念分析理论已经广泛地应用于计算机诸多领域.当前,模糊概念格直接构造仍然是该领域主要问题之一,其构造过程具有指数级时间复杂度.为了提高模糊概念格构造效率,文中对串行模糊概念构造算法进行并行化改进,将模糊集合组合搜索空间映射为自然数区间,简化了搜索空间表示、划分和遍历过程,进而提出并行模糊概念构造算法(Parallel Fuzzy Next Closure,ParaFuNeC).该算法对搜索空间均匀划分,子搜索空间彼此独立,从而避免并行任务之间同步、通讯等时间耗费,达到提高模糊概念构造效率的目标.时间复杂度分析和实验结果表明该算法在大规模计算任务情况下,加速比随着并行度的提高呈正比增长趋势.另外,串行比例指标表明ParaFuNeC算法在大规模计算任务情况下具有更好的可扩展性.
