IMPROVISASI BACKPROPAGATION MENGGUNAKAN PENERAPAN ADAPTIVE LEARNING RATE DAN PARALLEL TRAINING

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