Evolvable Rough-Block-Based Neural Network and its Biomedical Application to Hypoglycemia Detection System
Evolvable Rough-Block-Based Neural Network and its Biomedical Application to Hypoglycemia Detection System
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DOI：
10.1109/tcyb.2013.2283296
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发表时间：
2014
影响因子：
11.8
通讯作者：
P. P. San-P.;S. Ling;Nuryani;H. Nguyen
中科院分区：
文献类型：
--
作者：
P. P. San-P.;S. Ling;Nuryani;H. Nguyen
本文重点研究了利用粗糙集概念和神经计算进行决策和分类的混合技术。基于粗糙集的性质，定义了下界区域和边界区域，将输入信号划分为一致(可预测)部分和不一致(随机)部分。这样，神经网络被设计成只处理边界区域，该边界区域主要由应用输入信号的不一致部分组成，导致对数据集的不准确建模。由于神经网络应用的特点不同，传统神经网络的相同结构可能不能给出最优解。基于本文的应用知识，基于块的神经网络(BBNN)具有进化内部结构和适应动态环境的能力，被选为合适的分类器。该体系结构将系统地结合混合粗糙块神经网络(R-BBNN)的应用特点。将一种全局训练算法--小波变异混合粒子群算法用于R-BBNN的参数优化。通过将R-BBNN算法应用于医疗诊断领域，使用1型糖尿病患者的真实低血糖事件对该算法的性能进行了评估。将所提出的混合系统的性能与现有的一些神经网络进行了比较。比较结果表明，该方法提高了分类性能，并使网络提前收敛。
This paper focuses on the hybridization technology using rough sets concepts and neural computing for decision and classification purposes. Based on the rough set properties, the lower region and boundary region are defined to partition the input signal to a consistent (predictable) part and an inconsistent (random) part. In this way, the neural network is designed to deal only with the boundary region, which mainly consists of an inconsistent part of applied input signal causing inaccurate modeling of the data set. Owing to different characteristics of neural network (NN) applications, the same structure of conventional NN might not give the optimal solution. Based on the knowledge of application in this paper, a block-based neural network (BBNN) is selected as a suitable classifier due to its ability to evolve internal structures and adaptability in dynamic environments. This architecture will systematically incorporate the characteristics of application to the structure of hybrid rough-block-based neural network (R-BBNN). A global training algorithm, hybrid particle swarm optimization with wavelet mutation is introduced for parameter optimization of proposed R-BBNN. The performance of the proposed R-BBNN algorithm was evaluated by an application to the field of medical diagnosis using real hypoglycemia episodes in patients with Type 1 diabetes mellitus. The performance of the proposed hybrid system has been compared with some of the existing neural networks. The comparison results indicated that the proposed method has improved classification performance and results in early convergence of the network.