In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Kuala Lumpur, Malaysia was developed. Standard multilayered, feed-forward, back-propagation neural networks were designed using Microsoft Excel (MS Excel). The meteorological data were acquired from Malaysia Meteorological Department. The data was consists of meteorological data from one st...

This paper first proposes a new type of single-output Chebyshev-polynomial feed-forward neural network (SOCPNN) for pattern classification. A new type of multi-output Chebyshev-polynomial feedforward neural network (MOCPNN) is then proposed based on such an SOCPNN. Compared with multilayer perceptron, the proposed SOCPNN and MOCPNN have lower computational complexity and superior performance, s...

-The invasive measurement techniques which puncture the skin are used for blood data values detection generally. In this paper, artificial neural network structures were used for the classification of the relationship between blood data values and palm perspiration rate as a non-invasive measurement technique. For this purpose, a comparative study was realized by using feed forward multilayer, ...

We present in this paper a system of English handwriting recognition based on 26-point feature extraction of the character. Basically an off-line handwritten alphabetical character recognition system using multilayer feed forward neural network has been described in our work. Firstly a new method, called, 26-point feature extraction is introduced for extracting the features of the handwritten a...

This paper presents a method based on evolutionary computation to train multilayer morphological perceptron (MLMP). The algorithm calculates network parameters such as its connection weights, pre-synaptic and postsynaptic values for a given network topology. Morphological perceptron are a new type of feed-forward artificial neural network based on lattice algebra which can be used for pattern c...

This paper introduces a flexible neural tree (FNT) with necessary number of hidden units and is generated initially as a flexible multi-layer feed-forward neural network evolved using an evolutionary procedure and also considers the approximation of sufficiently smooth multivariable function with a multilayer perceptron. For a given neural tree with approximation order, explicit formulas for th...

This paper discusses the application of MultiLayer Feed Forward Neural Network (MFNN) for the differential protection of the turbogenerator based on pattern classification. The cases of all the possible internal faults in the stator of the generator with lap winding have been simulated using Modified Winding function Approach. The simulated fault currents in the phases and their parallel paths ...

Network motifs play an important role in the qualitative analysis and quantitative characterization of networks. The feed-forward loop is a semantically important and statistically highly significant motif. In this paper, we extend the definition of the feed-forward loop to subgraphs of arbitrary size. To avoid the complexity of path enumeration, we define generalized feed-forward loops as pair...

In the last several years, neural network models have significantly improved accuracy in a number of NLP tasks. However, one serious drawback that has impeded their adoption in production systems is the slow runtime speed of neural network models compared to alternate models, such as maximum entropy classifiers. In Devlin et al. (2014), the authors presented a simple technique for speeding up f...

A key goal in the study of visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. One way to guide the search for models is to use a general nonparametric regression algorithm, such as a neural network. We have developed a multilayer feed-forward network algorithm that can be used to characterize nonlinear stimulus-response ...