Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
J Zhejiang Univ Sci B. 2010 Feb;11(2):115-26. doi: 10.1631/jzus.B0910427.
In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.
本文提出并讨论了一种新颖的仿生模型及其在模式识别中的性能。该模型由一个球模型和一个三层皮质模型构建，模拟了嗅觉系统的主要特征。嗅觉球和皮质模型通过具有分布式延迟的前馈和反馈纤维连接。使用包含 683 名患者数据的乳腺癌威斯康星数据集，这些患者分为良性和恶性两类，以证明该模型即使在原始学习模式的变形版本下也具有学习和识别模式的能力。将新模型的性能与三个人工神经网络 (ANN) 进行了比较，包括反向传播网络、支持向量机分类器和径向基函数分类器。所有的神经网络和嗅觉仿生模型都在一个标准数据集的基准研究中进行了测试。实验结果表明，仿生嗅觉系统模型可以在小的训练集和少量的学习试验的基础上进行学习和分类，在一定程度上反映了生物智能。
