IMPROVISASI BACKPROPAGATION MENGGUNAKAN PENERAPAN ADAPTIVE LEARNING RATE DAN PARALLEL TRAINING
Abstract: Artificial neural
networks have long been used in the classification process, which offers the
flexibility of neural networks to the features of the object to be classified
and small storage space. The biggest drawback of the backpropagation network is
the time taken by the network to learn to be very long for large data
conditions of learning and the conditions in which the features between
different objects have small differences. To overcome the weaknesses of the
implementation of the development is carried out by applying the concept of
parallel adaptvie learning rate and training in order to improve the ability of
the network in the learning process.
Keywords: Character
Identification, Classification, Artificial Neural Network, Backpropagation,
Adaptive Learning Rate, Parallel Training
Penulis: Mufidah Khairani
Kode Jurnal: jptinformatikadd140218
