This paper presents an application of Neural Networks to the control of a real system with measurement noise. The details of the system and the implementation of sensor, controller and actuator are described. Saturation in the actuator is present and dealt with. The results of controlling the kiln with Direct Inverse Control and Additive Feedforward strategies are presented and compared. Proble...

This paper suggests a decision support system for tactical air combat environment where not much prior information is available about the decision regions. We proposed a combination of unsupervised learning for clustering the data (to develop decision regions) and a feed forward neural network to classify the decision regions accurately. The clustered data is used as the inputs to the multi-lay...

In this paper we implemented different models to solve the review usefulness classification problem. Both feed-forward neural network and LSTM were able to beat the baseline model. Performances of the models are evaluated using 0-1 loss and F-1 scores. In general, LSTM outperformed feed-forward neural network, as we trained our own word vectors in that model, and LSTM itself was able to store m...

In this paper, a thermal infra red face recognition system for human identification and verification using blood perfusion data and back propagation feed forward neural network is proposed. The system consists of three steps. At the very first step face region is cropped from the colour 24-bit input images. Secondly face features are extracted from the croped region, which will be taken as the ...

A single layer feed forward neural network algorithm using back propagation, gradient descent and weight decay is proposed for the purpose of wind speed forecasting using only the observed hourly wind speeds, directions, temperatures and pressures observed at at a single site. The site data used for this experiment was 10 years worth of hourly ASOS data from the Bismarck North Dakota Regional A...

In this paper is presented an investigation of the speech recognition classification performance. This investigation on the speech recognition classification performance is performed using two standard neural networks structures as the classifier. The utilized standard neural network types include Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions Neur...

Reclaimed asphalt pavement (RAP) is one of the waste materials that highway agencies promote to use in new construction or rehabilitation of highways pavement. Since the use of RAP can affect the resilient modulus and other structural properties of flexible pavement layers, this paper aims to employ two different artificial neural network (ANN) models for modeling and evaluating the effects of ...

Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer aided classification method in computed tomography (CT) images of lungs developed using artificial neural network. The entire lung is segmented from the CT images and the parameters are calculated from the segmented image. The statistical parameters like mean, standard ...

A three layer feed forward neural network was constructed and tested to analyze the scheduling process on single machine. The operating variables studied are the operation, processing time, setup time, deadline time, duedate time, priority, machine, and fabric color. These variables were used as input to the constructed neural network in order to predict the scheduling completion time as the ou...

The recurrent neural network is a feed-forward network ascribed to a parent neural network with feed-back connections (or in another term, oriented cycles). Its adaptation is performed by an analog of the standard back-propagation adaptation method. The recurrent neural network approach is illustrated by prediction and classification of 13C NMR chemical shifts in a series of monosubstituted ben...