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محسن زندی علی گنجلو ماندانا بی‌مکر

چکیده

در دهه‌های اخیر، از سیستم‌های هوش مصنوعی برای ایجاد مدل‌های پیش‌بینی جهت تخمین و پیش‌بینی بسیاری از فرآیندهای کشاورزی استفاده شده است. در این مطالعه، خصوصیات فیزیکی و شیمیایی میوه زالزالک طی نگهداری در شرایط مختلف با استفاده از شبکه‌های عصبی مصنوعی و سيستم استنتاج عصبي-فازي سازگار پیش‌بینی گردید. از داده‌های تجربی حاصل از نگهداری میوه، برای آموزش و آزمایش این شبکه‌ها استفاده شد. تعداد کل لایه‌های پنهان و تعداد نورون در هر لایه پنهان به روش سعی و خطا انتخاب گردید. شبکه عصبی و سیستم استنتاج عصبي-فازي سازگار طراحی شده دارای ورودی شامل زمان نگهداری، رطوبت اولیه و دمای نگهداری و یک متغیر در لایه‌های خروجی ( ، ، ،  و ) بود. مقادير R2 بالا و RMSE كم گوياي كارايي بالاي مدل شبكه عصبي مصنوعي و سیستم استنتاج عصبي-فازي سازگار در پيش‌بيني خصوصیات كيفي زالزالک طي فرآيند نگهداری مي‌باشد. نتایج نشان داد که شبکه عصبی پرسپترون چندلایه با الگوریتم یادگیری مومنتوم و تابع آستانه‌ای تان‌اکسون بهترین شبکه برای پیش‌بینی خصوصیات کیفی زالزالک در شرایط مختلف بود. نتایج مدل‌سازی با انفیس نشان داد که توابع عضویت ذوزنقه‌ای و گوسی بهترین عملکرد را به‌ترتیب در پیش‌بینی پارامترهای رنگی و فیزیکی داشت. با مقایسه نتایج حاصل از مدل‌سازی با شبکه عصبی مصنوعی و انفیس، تفاوت زیادی از نظر دقت و کارایی در پیش‌بینی مشاهده نشد، اگرچه شاخص RMSE در مدل‌سازی با کمک انفیس کمتر از شبکه عصبی مصنوعی بود که خود نمایان‌گر دقت بالاتر آن می‌باشد.

جزئیات مقاله

کلمات کلیدی

زالزالک, سيستم استنتاج عصبي-فازي سازگار, شبکه پرسپترون چند لایه‌ای, شبکه عصبی مصنوعی

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ارجاع به مقاله
زندیم., گنجلوع., & بی‌مکرم. (2020). به‌کارگیری سيستم استنتاج عصبي-فازي سازگار و شبکه‌های عصبی مصنوعی در پیش‌بینی و مدل‌سازی تغييرات كيفي زالزالک (Crataegus pinnatifida) طی شرایط مختلف انبارمانی. ماشین‌های کشاورزی, 11(2), 343-357. https://doi.org/10.22067/jam.v11i2.86654
نوع مقاله
مقاله علمی- پژوهشی