بررسی صفات رشد گوسفند لری با استفاده از مدل‌های غیر خطی و شبکه عصبی مصنوعی بهینه شده با الگوریتم ژنتیک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم دامی دانشگاه رامین خوزستان

2 گروه علوم دامی دانشکده کشاورزی دانشگاه لرستان

3 گروه گیاهپزشکی دانشگاه صنعتی شاهرود

چکیده

زمینه مطالعاتی: در این پژوهش از اطلاعات تعداد 7054 راس گوسفند نژاد لری برای برازش منحنی رشد این نژاد استفاده شد. هدف: صفات رشد مورد بررسی شامل وزن تولد، از شیرگیری، شش ماهگی و نه ماهگی بود که با استفاده از سه مدل غیر خطی شامل گمپرتز، برودی و لجستیک و همچنین شبکه عصبی مصنوعی (ANN) برازش شد. روش کار: تیپ تولد، جنسیت، سال تولد، سن مادر و فصل تولد به همراه وزن تولد، شیرگیری و شش ماهگی به عنوان عوامل ورودی به ANN معرفی شدند و برای وزن نه ماهگی پیش بینی انجام شد. برای این منظور یک شبکه Feed-forward بهینه شده با الگوریتم ژنتیک مورد استفاده قرار گرفت. مقایسه مدل­های غیرخطی بر اساس ضریب تبیین (R2)، میانگین مربعات خطا (MSE)، تعداد تکرار و معیار آکائیک (AIC) انجام شد و بر این اساس مدل برودی به عنوان مدل مناسب برای برازش صفات رشد انتخاب شد. پارامترهای A، B و K بر اساس مدل برودی برای دو جنس ماده و نر برآورد شدند. نتایج: همبستگی بین پارامترهای A و K منفی گزارش شد. اثر عوامل محیطی بر روی پارامترهای منحنی رشد معنی دار بود (01/0>P). بر اساس بررسی­های انجام شده ANN با R2 برابر با 36/84 و 49/85 درصد قادر به پیش بینی وزن نه ماهگی برای جنس ماده و نر بود. همچنین با تعداد 10 و 9 نورون در لایه میانی برای جنس ماده و نر، در MSE همگرایی ایجاد شد. نتیجه­گیری نهایی: بر اساس میزان R2 گزارش شده، مدل­های برودی، لجستیک، گمپرتز و ANN به ترتیب مناسب­ترین مدل­ها برای برازش صفات رشد در گوسفند لری بودند. 

کلیدواژه‌ها


عنوان مقاله [English]

Study of Lori growth traits using nonlinear models and artificial neural network optimized by genetic algorithm

نویسندگان [English]

  • F B 1
  • MT BN 1
  • A M 2
  • A Sh 3
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