Underwater estimation of audio signal prediction using fruit fly algorithm and hybrid wavelet neural network

被引：4
作者：
Sagayam K.M. [1 ]
Ghosh A. [2 ]
Bhushan B. [3 ]
Andrew J. [4 ]
Cengiz K. [5 ]
Elngar A.A. [6 ]
机构：
[1] Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore
[2] Department of Computational Science, Brainware University, Kolkata
[3] School of Engineering and Technology, Sharda University, Greater Noida
[4] Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore
[5] Department of Electrical and Electronics Engineering, Traka University, Edirne
[6] Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City
来源：
Journal of Reliable Intelligent Environments | 2022年 / 8卷 / 02期
关键词：
Accuracy; Forecasting; Fruit fly algorithm; Recognition rate; Sound signal; Wavelet neural network;
D O I：
10.1007/s40860-021-00151-4
中图分类号：
学科分类号：
摘要：
Underwater acoustic signal is one of its most vital features in oceanography for predicting the local variants, helps to process the signal, and also assists to discover non-stationary signals. wavelet neural network (WNN) architecture acquires a huge benefit from the artificial neural network (ANN) and time-frequency localizing features of wavelet analysis (WA). However, the system could train using the ANN model, it has tendency of falling into local minima and generating convergence. A wavelet-aided neural network is integrated with a meta-heuristic optimizer named fruit fly algorithm (FFA) that aids our proposed system in which reduces the computational complexity and improves performance measures. The main objective of the proposed work is of modelling an optimized wavelet neural network for efficient measurement of the mean absolute error and the root mean square error. The simulation of the sound signals has been performed with the use of optimized neural networks that has predicted the unknown signal. The proposed model is assessed by comparing the outcomes with the complements of it, i.e. the traditional neural networks. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
引用
收藏
页码：211 / 221
页数：10
相关论文
共 33 条
  • [1] Wu Y.-N., Sun Y., Hu Z.-X., Research on the non-linear modelling and prediction method of underwater acoustic signals, 2014 12Th International Conference on Signal Processing (ICSP), Pp, pp. 2163-2168, (2015)
  • [2] ICSP’98.1998 fourth international conference on signal processing, August, (2002)
  • [3] Yang M., Li X., Yang Y., Meng X., Characteristic analysis of underwater acoustic scattering echoes in the wavelet transform domain, Harbin Engineering University and Springer, pp. 93-101, (2017)
  • [4] Boashash B (2002) Detection of underwater transient acoustic signals using time-frequency distributions and higher-order spectra, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems and Computers, Pp 1103–1107, 06, (2002)
  • [5] Adamowski J., Sun K., Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds, J Hydrol, 390, 1-2, pp. 85-91, (2010)
  • [6] Falamarzia Y., Palizdana N., Huangb Y.F., Lee T.S., Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs), Agric Water Manag, 140, pp. 26-36, (2014)
  • [7] Li S., Tuteur F.B., Estimation of underwater source parameters by use of multipath information, Fourth Annual ASSP Workshop on Spectrum Estimation and Modelling, IEEE, pp. 258-263, (2002)
  • [8] Li D.-Q., Hallander J., Johansson T., Predicting underwater radiated noise of a full-scale ship with model testing and numerical methods, Engineering, 161, pp. 121-135, (2018)
  • [9] Lei W., Liu Q., Tian X., Zhang J., Xiao W., A new improved fruit fly algorithm IAFFA and its application to solve engineering optimization problems, Knowl Based Syst, 144, 15, pp. 153-173, (2018)
  • [10] Pan W.-T., A new fruit fly algorithm: taking the financial distress model as an example, Knowl-Based Syst, 26, pp. 69-74, (2012)
1234