基于粒子群优化算法的PSO-BP海底声学底质分类方法
发布时间：2018-08-20 18:16
【摘要】：利用粒子群优化算法(PSO)较强的鲁棒性和全局搜索能力等优点,将PSO算法与BP神经网络相结合,优化了BP神经网络分类时的初始权值和阈值。基于珠江河口三角洲的侧扫声呐图像数据,提取了海底声呐图像中砂、礁石、泥3类典型底质的6种主要特征向量,利用PSO-BP方法对海底底质进行分类识别。实验表明,3类底质分类精度均大于90%,高于BP神经网络70%左右的分类精度,表明PSO-BP方法可有效应用于海底底质的分类识别。
[Abstract]:Based on the strong robustness and global searching ability of particle swarm optimization (PSO) algorithm, the initial weights and thresholds of BP neural network classification are optimized by combining PSO algorithm with BP neural network. Based on the side scan sonar image data of the Pearl River estuary delta, six main characteristic vectors of sand, reef and mud are extracted from the seabed sonar image, and the seabed sediment is classified and recognized by PSO-BP method. The experimental results show that the classification accuracy of the three types of sediments is more than 90%, which is higher than that of BP neural network about 70%. It shows that the PSO-BP method can be applied to the classification and recognition of seabed sediment effectively.
【作者单位】： 国家海洋局第二海洋研究所;国家海洋局海底科学重点实验室;浙江大学地球科学学院;
【基金】：国家自然科学基金(41476049) 科技基础性工作专项(2013FY112900) 海洋公益项目(201105001)
【分类号】：P733.2
，
本文编号：2194559
[Abstract]:Based on the strong robustness and global searching ability of particle swarm optimization (PSO) algorithm, the initial weights and thresholds of BP neural network classification are optimized by combining PSO algorithm with BP neural network. Based on the side scan sonar image data of the Pearl River estuary delta, six main characteristic vectors of sand, reef and mud are extracted from the seabed sonar image, and the seabed sediment is classified and recognized by PSO-BP method. The experimental results show that the classification accuracy of the three types of sediments is more than 90%, which is higher than that of BP neural network about 70%. It shows that the PSO-BP method can be applied to the classification and recognition of seabed sediment effectively.
【作者单位】： 国家海洋局第二海洋研究所;国家海洋局海底科学重点实验室;浙江大学地球科学学院;
【基金】：国家自然科学基金(41476049) 科技基础性工作专项(2013FY112900) 海洋公益项目(201105001)
【分类号】：P733.2
，
本文编号：2194559
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