به‌کارگیری سیستم استنتاج عصبی-فازی سازگار و شبکه‌های عصبی مصنوعی در پیش‌بینی و مدل‌سازی تغییرات کیفی زالزالک (Crataegus pinnatifida) طی شرایط مختلف انبارمانی

ماشین های کشاورزی

مقاله 16، دوره 11، شماره 2 - شماره پیاپی 22، 1400، صفحه 343-357   

نوع مقاله: مقاله پژوهشی

شناسه دیجیتال (DOI): 10.22067/jam.v11i2.86654

نویسندگان

محسن زندی^* ؛ علی گنجلو؛ ماندانا بی‌مکر

گروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران

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 سیستم استنتاج عصبی-فازی 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 عصبی-فازی سازگار طراحی شده دارای ورودی شامل زمان نگهداری، رطوبت اولیه و دمای نگهداری و یک متغیر در لایه‌های خروجی (WL، F،c* ، *h و RPI) بود. مقادیر R² بالا و RMSE کم گویای کارایی بالای مدل شبکه عصبی مصنوعی و سیستم استنتاج عصبی-فازی سازگار در پیش‌بینی خصوصیات کیفی زالزالک طی فرآیند نگهداری می‌باشد. 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.

کلیدواژه‌ها

زالزالک؛ سیستم استنتاج عصبی-فازی سازگار؛ شبکه پرسپترون چند لایه‌ای؛ شبکه عصبی مصنوعی

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