The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were ...

We consider two-layered perceptions consisting of N binary input units, K binary hidden units and one binary output unit, in the limit N >> K > or = 1. We prove that the weights of a regular irreducible network are uniquely determined by its input-output map up to some obvious global symmetries. A network is regular if its K weight vectors from the input layer to the K hidden units are linearly...

Adaptative training of the non-linear single-layer perceptron can lead to the Euclidean distance classifier and later to the standard Fisher linear discriminant function. On the way between these two classifiers one has a regularized discriminant analysis. That is equivalent to the “weight decay” regularization term added to the cost function. Thus early stopping plays a role of regularization ...

The nearest-neighbor multilayer perceptron (NN-MLP) is a single-hidden-layer network suitable for pattern recognition. To design an NN-MLP efficiently, this paper proposes a new evolutionary algorithm consisting of four basic operations: recognition, remembrance, reduction, and review. Experimental results show that this algorithm can produce the smallest or nearly smallest networks from random...

In this paper, a multilayer perceptron guided encryption/decryption (STMLP) in wireless communication has been proposed for exchange of data/information. Multilayer perceptron transmitting systems at both ends generate an identical output bit and the network are trained based on the output which is used to synchronize the network at both ends and thus forms a secret-key at end of synchronizatio...

In this study, the application of a combined segmentation method using the CannyDeriche filter and a Multi Layer Perceptron neural network is considered. The segmentation of five LANDSAT spectral bands is conducted. Obtained segmented images are combined using a multi experts approach in order to improve the segmentation quality and to preserve the land cover regions.

Air pollution emerges as an imminent issue in metropolitan cities like Hong Kong, and attracts much attention in recent years. Prediction of pollutant levels and their tendency is an important topic in environmental science today. To achieve such prediction tasks, the use of neural network (NN), in particular, the multi-layer perceptron, is regarded as a cost-e7ective technique superior to trad...

A novel neural-network based technique is described for the remote condition-monitoring of an in-service gas-turbine flowmeter. The method uses a C language implementation of a modified multi-layer perceptron (MLP) neural networks, which enables detection of the accumulation of contaminating material on the rotor blades that could lead to changes in meter-factor and loss of calibration.

In this paper, we propose a deep neural network based natural language processing system for semantic textual similarity prediction. We leverage multi-layer bidirectional LSTM to learn sentence representation. After that, we construct matching features followed by Highway Multilayer Perceptron to make predictions. Experimental results demonstrate that this approach can’t get better results on s...