Abstract
This paper analyzes the operation principle and predicted value of the recurrent-neural-network (RNN) structure, which is the most basic and suitable for the change of time in the structure of a neural network for various types of artificial intelligence (AI). In particular, an RNN in which all connections are symmetric guarantees that it will converge. The operating principle of a RNN is based on linear data combinations and is composed through the synthesis of nonlinear activation functions. Linear combined data are similar to the autoregressive-moving average (ARMA) method of statistical processing. However, distortion due to the nonlinear activation function in RNNs causes the predicted value to be different from the predicted ARMA value. Through this, we know the limit of the predicted value of an RNN and the range of prediction that changes according to the learning data. In addition to mathematical proofs, numerical experiments confirmed our claims.
Highlights
Artificial intelligence (AI) with machines are coming into our daily lives
We interpreted the structure of the underlying the recurrent neural network (RNN) and, on this basis, we found the principles that the RNN could predict
A basic RNN works like a time series in a very narrow range of variables
Summary
Artificial intelligence (AI) with machines are coming into our daily lives. In the near future, there will be no careers in a variety of fields, from driverless cars becoming commonplace, to personalroutine assistants, automatic response system (ARS) counsellors, and bank clerks. This methodology learns about time changes and predicts them This predictability is possible because of the recurrent structure, and it produces similar results as the time series of general statistical processing [9,10,11,12]. The RNN calculation method is very similar to that of the time series, but the activation function in a neural-network (NN) structure is a nonlinear function, so nonlinear effects appear in the prediction part. For this reason, it is very difficult to find the predicted value of a RNN. Due to the advantages of the recurrent structure and the development of artificial-neural-network (ANN) calculation methods, the accuracy of predicted values is improving.
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