基于SOM的水声数据可视化算法优化研究

Research on optimization of underwater acoustic data visualization algorithm based on SOM

传统的水声数据算法存在算法结构计算不够明确,在数据计算中,无法准确对数据进行环节清晰展现,导致数据结果无法进行反向验证的问题。因此提出基于SOM的水声数据可视化算法优化研究。通过引入SOM神经水声数据分析算法,对水声数据进行神经元数据分析,完成对水声数据的预分析;接着,引入水声特征直方图分类算法,对完成预分析计算后的水声数据进行直方图特征分类计算,清晰数据类别;最后,引入可视化算法的引入与执行计算,将分类后的水声数据进行可视化转换计算,完成对传统算法整体优化;通过设计的仿真实验,证明提出优化方法的有效性与可行性。

The traditional underwater acoustic data algorithm has the problem that the calculation of algorithm structure is not clear enough.In the data calculation,the data can not be displayed clearly and accurately,which leads to the problem that the data results can not be reversed validated.Therefore,the optimization of underwater acoustic data visualization algorithm based on SOM is proposed.By introducing SOM neural underwater acoustic data analysis algorithm,the neuron data analysis of underwater acoustic data is carried out to complete the pre-analysis of underwater acoustic data.Then,the underwater acoustic feature histogram classification algorithm is introduced to classify and calculate the underwater acoustic data after the pre-analysis and calculation,so as to clarify the data categories.The introduction and execution of visualization algorithm,the visualization conversion calculation of the classified underwater acoustic data,completes the overall optimization of the traditional algorithm,and proves the effectiveness and feasibility of the proposed optimization method through the design of simulation experiments.