The article deals with a WAN switch design based on a Feedforward neural network, specifically for the Feedforward Backpropagation algorithm. The designed switch is fully parallel, uses neural network for switch management and also for traffic engineering. The switch uses advanced packet dropping mechanism. The article describes the switch design (network processor design) and compares the deve...

The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates static neural approach by applying Modular feedforward neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using static modular neural network modeling. Methodologies and techniq...

Abs t rac t . Approaches to data mining proposed so far are mainly symbolic decision trees and numerical feedforward neural networks methods. While decision trees give, in many cases, lower accuracy compared to feedforward neural networks, the latter show black-box behaviour, long training times, and difficulty to incorporate available knowledge. We propose to use an incrementally-generated rec...

Spike-timing dependent plasticity (STDP) is an organizing principle of biological neural networks. While synchronous firing of neurons is considered to be an important functional block in the brain, how STDP shapes neural networks possibly toward synchrony is not entirely clear. We examine relations between STDP and synchronous firing in spontaneously firing neural populations. Using coupled he...

We introduce a new structure for memory neural networks, called feedforward sequential memory networks (FSMN), which can learn long-term dependency without using recurrent feedback. The proposed FSMN is a standard feedforward neural networks equipped with learnable sequential memory blocks in the hidden layers. In this work, we have applied FSMN to several language modeling (LM) tasks. Experime...

This paper presents a feedforward neural network approach to sunspot forecasting. The sunspot series were analyzed with feedforward neural networks, formalized based on statistical models. The statistical models were used as comparison models along with recurrent neural networks. The feedforward networks had 24 inputs (depending on the number of predictor variables), one hidden layer with 20 ...

In this study, we aimed to develop an automatic atrial fibrillation detection technique for the early prediction of fibrillation, that can be used with wearable devices. An effective deep learning-based technology is proposed automatically detect fibrillation. First, novel preprocessing algorithms, wavelet transform and sliding window filtering, are introduced reduce noise outliers, respectivel...

Logistic regression LR is a conventional statistical technique used for data classification problem. Logistic regression is a model-based method, and it uses nonlinear model structure. Another technique used for classification is feedforward artificial neural networks. Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function. ...

objective(s): this study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ann) and correlation using experimental data. methods: two-layer perceptron feedforward artificial neural network and backpropagation levenberg-marquardt (bp-lm) training algorithm are used to predict the thermal cond...