The conventional linear backpropagation algorithm is replaced by a nonlinear version, which avoids the necessity for calculating the derivative of the activation function. This may be exploited in hardware realizations of neural processors. In this paper we derive the nonlinear backpropagation algorithms in the framework of recurrent backpropagation and present some numerical simulations of fee...

The widely used backpropagation algorithm based on stochastic gradient descent suffers from typically slow convergence to either local or global minimum error. This backpropagation algorithm bears great resemblance to a classic proportional integral derivative (PID) control system. Fractional calculus shows promise for improving stability and response in feedback control through the use of non-...

The backpropagation artificial neural network (ANN) is a well-known and widely applied mathematical model for remote sensing applications for pattern recognition, approximation and mapping of non-linear functions and time-series prediction. The backpropagation ANN algorithm is underpinned by a gradient descent algorithm that is used to modify the network weights to maximise performance, using s...

background : the study examined the implementation of artificial neural network (ann) for the prediction of ammonia nitrogen removal from landfill leachate by ultrasonic process. methods : a three-layer backpropagation neural network was optimized to predict ammonia nitrogen removal from landfill leachate by ultrasonic process. considering the smallest mean square error (mse), the configuration...

In this paper, we explore the parallel implementation of the backpropagation algorithm with and without hidden layers on MasPar MP-1. This implementation is based on a SIMD architecture, and uses a backpropagation model. Our implementation uses weight batching versus on-line updating of the weights which is used by most serial and parallel implementations of backpropagation. This method results...

Network-based computer systems play increasingly vital roles in modern society; they have become the target of intrusions by our enemies and criminals. Intrusion detection system attempts to detect computer attacks by examining various data records observed in processes on the network. This paper presents a hybrid intrusion detection system models, using Learning Vector Quantization and an enha...

Learning process is essential for good performance when a neural network is applied to a practical application. The backpropagation algorithm [1] is a well-known learning method widely used in most neural networks. However, since the backpropagation algorithm is time-consuming, much research have been done to speed up the process. The block backpropagation algorithm, which seems to be more effi...