Aktivite Tanımada Yapay Sinir Ağları ve Genetik Programlama Yöntemlerinin Karşılaştırılması

Erdaş Ç. B., Aşuroğlu T., Açıcı K., Oğul H.

Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu BİLDİRİLER KİTABI, cilt.1, sa.1, ss.62-66, 2018 (Düzenli olarak gerçekleştirilen hakemli kongrenin bildiri kitabı)

Özet

With the widespread use of wearable sensors, the processing of raw data obtained from sensors has led to widely-used solutions to the problem of activity recognition. In this context, it is aimed to compare the performance of artificial neural network methods (ANN, RBFNN) and genetic programming (GP) methods over time, frequency and wavelet features extracted from the accelerometer data. The most successful classification performance achieved was 75.09% using 31 neurons in the hidden layer of the multilayer perceptron, using time attributes.