Affiliation:
1. Department of Information Technology, College of Engineering and Computer Science, Sari Branch, Islamic Azad University, Sari, Iran
2. Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
3. ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, Madrid 28040, Spain
4. Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq
Abstract
The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activist fields. In this paper, first, an experimental data set was created using a designed scenario. The whale optimization algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference by setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, and PGO were also used for MLPNN training. The measured criteria are concurrency speed, ability to avoid local optimization, and the classification rate. The simulation results on the obtained data set showed that, respectively, the classification rate in MLPFWOA, MLP-CVOA, MLP-WOA, MLP-ChOA, MLP-BWO, and MLP-PGO classifiers is equal to 94.98, 92.80, 91.34, 90.24, 89.04, and 88.10. As a result, MLP-FWOA performed better than other algorithms.
Subject
General Mathematics,General Medicine,General Neuroscience,General Computer Science
Cited by
5 articles.
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