Schliebs Stefan, Defoin-Platel Michaël, Worner Sue, Kasabov Nikola
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand.
Neural Netw. 2009 Jul-Aug;22(5-6):623-32. doi: 10.1016/j.neunet.2009.06.038. Epub 2009 Jul 2.
This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naïve Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
本研究引入了一种受量子启发的脉冲神经网络（QiSNN）作为一种集成连接主义系统，其中，进化脉冲神经网络的特征和参数通过使用受量子启发的进化算法一起进行优化。我们在此提出一种新颖的优化方法，该方法使用不同的表示来探索两个搜索空间：用于优化特征子集的二进制表示和用于进化脉冲网络合适实值配置的连续表示。在两个不同的合成基准数据集上研究了改进框架的属性和特征。将结果与传统方法进行比较，即多层感知器和朴素贝叶斯分类器（NBC）。重新审视了之前使用的关于入侵物种建立预测的真实世界生态数据集，并由一位生态专家获得并分析了新结果。所提出的方法能够更快地收敛到最优解（或接近最优解），具有更高的准确性，并且能够选择更具信息量的特征集。
