1 Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho Naka-ku, Sakai-Shi, Osaka 599-8531, Japan.
2 Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
Int J Neural Syst. 2019 Jun;29(5):1850052. doi: 10.1142/S0129065718500521. Epub 2018 Oct 30.
This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.
本文试图解决自组织生长网络模型的典型问题，即(a)输入数据的顺序对自组织能力的影响，(b)对高维数据的不稳定性和对噪声的过度敏感性，以及(c)通过将核贝叶斯规则（KBR）和相关熵诱导度量（CIM）集成到自适应谐振理论（ART）框架中，计算成本过高。KBR 执行无协方差的贝叶斯计算，能够保持快速稳定的计算。CIM 是一种广义的相似性度量，即使在高维空间中也能保持高降噪能力。此外，基于生长神经网络（GNG）的拓扑结构构建过程被集成到 ART 框架中，以增强其自组织能力。使用合成和真实数据集的仿真实验表明，所提出的模型具有出色的稳定自组织能力，适用于各种测试环境。
