طراحی سامانه سوناری با قابلیت دسته‌بندی اهداف فعال و غیرفعال آکوستیکی مبتنی بر شبکه‌ی عصبی فراابتکاری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه علوم دریایی امام خمینی (ره) نوشهر

2 دانشگاه علوم دریایی امام خمینی

چکیده

با توجه به اهمیت تعیین ماهیت اهداف سوناری در نبردهای دریایی، این مقاله به طراحی سامانه‌ای باقابلیت دسته­بندی اهداف سوناری فعال و غیرفعال با استفاده از شبکه­های عصبی پرسپترون چندلایه می‌پردازد. با توجه به نواقص پرسپترون­های چندلایه در کار با داده­های دنیای واقعی، این مقاله یک الگوریتم فرا ابتکاری جدید بانام بهینه‌ساز ازدحام ذرات با گروهای آشفته (CGPSO) را ارائه می­دهد. این الگوریتم با استفاده از گروه­های مجزای جستجو و همچنین نقشه­های آشفته فضای جستجو را بهتر و سریع­تر اکتشاف می­کند. به‌منظور ارزیابی سامانه طراحی‌شده، یک مجموعه داده سوناری مرجع، یک مجموعه داده آزمایشگاهی غیرفعال و یک مجموعه داده واقعی فعال توسعه داده شد. به‌منظور داشتن یک مقایسه جامع سامانه طراحی‌شده با بهینه‌سازهای معیار ازدحام ذرات (PSO)، جغرافیایی زیستی (BBO) و گرگ خاکستری (GWO) ازنظر سرعت همگرایی، دقت دسته‌بندی و قابلیت اعتماد مقایسه شد که سامانه طراحی‌شده نسبت به بهترین دسته­بندی کننده موجود، به­طور میانگین 33/2 درصد دقیق­تر عمل کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Designing a Sonar System with the Ability of Classifying Active and Passive Acoustic Targets Based on the Evolutionary Neural Network

نویسندگان [English]

  • Mohammad Khishe 1
  • Ehsan Ebrahimi 2
  • Afshin Goldani 2
1 Iran university of science and technology
2 Marine science university of Imam Khomeini
چکیده [English]

Considering the importance of identifying and determining the nature of the sonar targets in marine battles, the purpose of this paper is to design a system with the ability to classify active and passive sonar targets using multi-layer perceptron neural networks (MLP NNs). Considering the defects of MLP NNs in dealing with real-world data, as well as low classification accuracy and low convergence rate, this paper proposes a new meta-parasitic algorithm called Chaotic Groups Particles Swarm Optimizer (CGPSO) to train an MLP NN. This algorithm explores the search space faster and better than normal particle swarm Optimizer (PSO) using chaotic and independent groups. To evaluate the designed system, a benchmark sonar dataset, a passive laboratory data set and an active dataset were developed. In order to have a comprehensive comparison, the designed system was compared with PSO, biogeography-based Optimizer (BBO) and Gray Wolf Optimizer (GWO) in terms of convergence rate, classification accuracy, and reliability. Results show that the designed system was more accurate than the best available classifier, by average 2.33%.

کلیدواژه‌ها [English]

  • Sonar
  • Classification
  • Multi-layer Perceptron
  • Chaotic Particle Swarm Optimization
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