نوع مقاله : مقاله پژوهشی
نویسندگان
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.
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