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This paper deals with a design methodology for a neural network with improved robust qualities in notion to handling uncertain input data space variations. The proposed network topology combines the simplicity of the radial basis functions networks to interpret or classify data pairs and the abilities of the intuitionistic fuzzy logic to deal with the vagueness of the data space. A simplified gradient...
This paper presents two parallel implementationsof the Back-propagation algorithm, a widely used approach forArtificial Neural Networks (ANNs) training. These implementationspermit one to increase the number of ANNs trainedsimultaneously taking advantage of the thread-level massiveparallelism of GPUs and multi-core architecture of modernCPUs, respectively. Computational experiments are carried outwith...
Off-line pattern recognition in speech signals is a complex task. Yet, this task becomes harder when the recognition result is required online. The present work proposes an online identification of the Portuguese language phonemes using an nonlinear autoregressive model with exogenous inputs, commonly called NARX. The process first extracts the frequency characteristics of the input speech signals...
Numerical simulation methods like the finite element method lead to large systems of linear equations solved with well-known methods. Their performance varies depending on the considered simulation (discretization and physics) and the available hardware. To predict a suitable method including the solver and a well performing preconditioner, a feed-forward neural network is used. It computes performance...
A hybrid algorithm for short-term load forecasting is proposed. The particle swarm optimization algorithm used in the training phase of the artificial neural network is optimized by combining it with the gravitational search algorithm. In this paper, we have combined the exploitation of PSO and exploration of GSA to form a single algorithm that can be used to get more accurate results for load forecast...
In this paper, a maximum sensibility neural network is proposed to make an online learning system of a inverse controller of a plant. This neural network is trained to learn the response of the plant to different random inputs. Once the network is trained, it can be used to control the plant to a desired output.
This paper investigates the design of a multivariate B-spline neural network using the orthogonal least squares algorithm for non-linear system identification. The B-spline neural network is a type of basis function neural network which has been developed from the function approximation approach based on B-spline functions. Usually, this kind of neural network is trained using the gradient-based algorithm...
Security is still a major concern in Cloud computing, especially the detection of nefarious use or abuse of cloud instances. One reason for this, is the ever-growing complexity and dynamic of the underlying system design and architecture. To be able to detect misuse of cloud instances, this work presents an anomaly detection system for Infrastructure as a Service Clouds. It is based on Cloud customers'...
Development of modern technologies is related to an increasing complexity of the objects controled and hence the systems controlling them. In the most cases, automatic control systems consist of different nonlinear elements that significantly limit the capabilities of classical control theory in designing controllers. In recent decades, the methodology of neural networks has been increasingly used...
The opportunity of application neuronetwork technologies in a IP-telephony is considered. The basic stages of an engineering technique of construction neuronetwork models are selected on the basis of an adaptive neural network.
In this paper, we introduce the use of limited memory modified BFGS method (L-MBFGS) to improve the efficiency of training algorithms for feedforward neural networks. The quadratic termination property of L-MBFGS algorithm is given which is an important quasi-Newton property.
A new method of neural networks based on genetic algorithm is put forward for factors weight determination of safety assessment in the paper. The procedure on optimizing neural networks by genetic algorithm is expatiated. How to pick up the information of factors weight from the network link weight after training is analyzed in detail. The influence of primary network weight on final determination...
The accurate and reliable Trip-generation Forecasting Model is the most basic and important part of the traffic forecasting model. This paper focuses on combining the neural network which has a strong fitting capability and genetic algorithm which has an excellent Global search capability with trip-generation forecasting model in order to achieve the purpose of improving the accuracy of prediction...
Neural networks technology has been applied in many fields successfully, under common implementation environment of symbol computation system, for each intelligent technique, they could just reinforce each other except for taking place. This paper will mainly discuss some hot branches in hybrid applications of intelligent techniques with neural networks technology, and its implementation. From large...
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