دانلود مقاله ISI انگلیسی شماره 53007
عنوان فارسی مقاله

یک مدل خطی بر اساس فیلتر کالمن برای بهبود عملکرد طبقه بندی شبکه عصبی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
53007 2016 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A linear model based on Kalman filter for improving neural network classification performance
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 49, 1 May 2016, Pages 112–122

کلمات کلیدی
شبکه عصبی؛ مدل خطی؛ فیلتر کالمن - عملکرد طبقه بندی
پیش نمایش مقاله
پیش نمایش مقاله یک مدل خطی بر اساس فیلتر کالمن برای بهبود عملکرد طبقه بندی شبکه عصبی

چکیده انگلیسی

Neural network has been applied in several classification problems such as in medical diagnosis, handwriting recognition, and product inspection, with a good classification performance. The performance of a neural network is characterized by the neural network's structure, transfer function, and learning algorithm. However, a neural network classifier tends to be weak if it uses an inappropriate structure. The neural network's structure depends on the complexity of the relationship between the input and the output. There are no exact rules that can be used to determine the neural network's structure. Therefore, studies in improving neural network classification performance without changing the neural network's structure is a challenging issue. This paper proposes a method to improve neural network classification performance by constructing a linear model based on the Kalman filter as a post processing. The linear model transforms the predicted output of the neural network to a value close to the desired output by using the linear combination of the object features and the predicted output. This simple transformation will reduce the error of neural network and improve classification performance. The Kalman filter iteration is used to estimate the parameters of the linear model. Five datasets from various domains with various characteristics, such as attribute types, the number of attributes, the number of samples, and the number of classes, were used for empirical validation. The validation results show that the linear model based on the Kalman filter can improve the performance of the original neural network.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.