کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6862945 1439399 2018 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A frequency-domain approach to improve ANNs generalization quality via proper initialization
ترجمه فارسی عنوان
یک روش فرکانس دامنه برای بهبود کیفیت عمومی با استفاده از ابتدایی مناسب
کلمات کلیدی
هوش مصنوعی، تابع پاسخ فرکانس، پروپترون چند لایه تعمیم شبکه، راه اندازی شبکه،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
The ability to train a network without memorizing the input/output data, thereby allowing a good predictive performance when applied to unseen data, is paramount in ANN applications. In this paper, we propose a frequency-domain approach to evaluate the network initialization in terms of quality of training, i.e., generalization capabilities. As an alternative to the conventional time-domain methods, the proposed approach eliminates the approximate nature of network validation using an excess of unseen data. The benefits of the proposed approach are demonstrated using two numerical examples, where two trained networks performed similarly on the training and the validation data sets, yet they revealed a significant difference in prediction accuracy when tested using a different data set. This observation is of utmost importance in modeling applications requiring a high degree of accuracy. The efficiency of the proposed approach is further demonstrated on a real-world problem, where unlike other initialization methods, a more conclusive assessment of generalization is achieved. On the practical front, subtle methodological and implementational facets are addressed to ensure reproducibility and pinpoint the limitations of the proposed approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 104, August 2018, Pages 26-39
نویسندگان
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