Providing credit has become a main source of profit for financial and non-financial institutions. However, this transaction might lead into risk. This risk occurred if debtors unable to complete their obligations that will led loss creditors. It is necessity company create assessment in distinguishing eligible or non-eligible prospective customer. Artificial Neural Network (ANN) introduced solv...

A computer model of the feed-forward neural network with the hidden layer is developed to reconstruct physical field investigated by the fiber-optic measuring system. The Gaussian distributions of some physical quantity are selected as learning patterns. Neural network is learned by error backpropagation using the conjugate gradient and coordinate descent minimization of deviation. Learned neur...

Acrylic polymer that is highly stable against chemicals and is a good choice when concrete is subject to chemical attack. In this study, self-compacting concrete (SCC) made using acrylic polymer, nanosilica and microsilica has been investigated. The results of experimental testing showed that the addition of microsilica and acrylic polymer decreased the tensile, compressive and bending strength...

Building on earlier work extending Sigma’s mixed (symbols + probabilities) graphical band to inference in feedforward neural networks, two forms of neural network learning – target propagation and backpropagation – are introduced, bringing Sigma closer to a full neural-symbolic architecture. Adapting Sigma’s reinforcement learning (RL) capability to use neural networks in policy learning then y...

In this paper we report results for the prediction of thermodynamic properties based on neural networks, evolutionary algorithms and a combination of them. We compare backpropagation trained networks and evolution strategy trained networks with two physical models. Experimental data for the enthalpy of vaporization were taken from the literature in our investigation. The input information for b...

This paper describes the development of neural network models for noise reduction. The networks used to enhance the performance of modeling captured signals by reducing the effect of noise. Both recurrent and multi-layer Backpropagation neural networks models are examined and compared with different training algorithms. The paper presented is to illustrate the effect of training algorithms and ...

This paper proposes an extension of neural network identification capabilities for on-line identification of a nonlinear closed-loop control system. The neural network (NN) is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal control input. The results confirm the ef...

Using the backpropagation algorithm, we have trained the feed forward neural network to pronounce Polish language, more precisely to translate Polish text into its phonematic counterpart. Depending on the input coding and network architecture, 88%-95% translation eeciency was achieved.