کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
404627 677441 2009 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel multi-epitopic immune network model hybridized with neural theory and fuzzy concept
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
A novel multi-epitopic immune network model hybridized with neural theory and fuzzy concept
چکیده انگلیسی

The natural immune system provides an effective defense mechanism against foreign substances via complex interactions among various cells and molecules. Jerne introduced the immune network theory to model the relation between immune cells and molecules. The immune system like the neural system is able to learn from experience. In this paper, a multi-epitopic immune network model is proposed. The proposed model is hybridized with Learning Vector Quantization (LVQ) and fuzzy set theory to present a new supervised learning method. The new method is called Hybrid Fuzzy Neuro-Immune Network based on Multi-Epitope approach (HFNINME). To evaluate the performance of the proposed method several experiments on benchmark classification problems are carried out and the results are compared with two prominent immune-based classifiers as well as several versions of the LVQ algorithm. The results of the experiments reveal that the proposed method yields a parsimonious classifier that can classify data more accurately and more efficiently.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 22, Issues 5–6, July–August 2009, Pages 633–641
نویسندگان
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