Empirical models artificial neural networks

In the chemical engineering field, there are numerous processes and systems for which the physical and chemical laws that govern them are not fully known, unreliable knowledge being available. In such cases, it can either be that phenomenological models have not been developed, or the obtained models are affected by significant errors. Empirical modeling, based on experimental data, becomes a useful tool, with real opportunities to provide precise models. Artificial neural networks (ANN) and... 

As a nonlinear problem, predicting nonwoven properties fix)m the processing parameters and structural characteristics can be realized by an empirical modeling method that includes the statistical model, artificial neural network (ANN) model and others. ANN models have been shown to provide good approximations in presence of noisy data and smaller number of experimental points and the assumptions imder which ANN models work are less strict than those for statistical models [1]. 

Artificial Neural Networks as a Semi-Empirical Modeling Tool for Physical Property Predictions in Polymer Science... 

Recently, a new approach called artificial neural networks (ANNs) is assisting engineers and scientists in their assessment of fuzzy information, Polymer scientists often face a situation where the rules governing the particular system are unknown or difficult to use. It also frequently becomes an arduous task to develop functional forms/empirical equations to describe a phenomena. Most of these complexities can be overcome with an ANN approach because of its ability to build an internal model based solely on the exposure in a training environment. Fault tolerance of ANNs has been found to be very advantageous in physical property predictions of polymers. This chapter presents a few such cases where the authors have successfully implemented an ANN-based approach for purpose of empirical modeling. These are not exhaustive by any means. 

Such applications of NN as a predictive method make the artificial neural networks another technique of data treatment, comparable to parametric empirical modeling by, for example, numerical regression methods [e.g., 10,11] briefly mentioned in section 16.1. The main advantage of NN is that the network needs not be programmed because it learns from sets of experimental data, which results in the possibility of representing even the most complex implicit functions, and also in better modeling without prescribing a functional form of the actual relationship. Another field of... 

J. G. Magallanes, P. Smichowski and J. Marrero, Optimisation and empirical modeling of HG-ICP-AES analytical technique through artificial neural networks, J. Chem. Inf. Comput. Sci., 41(3), 2001, 824-829. 

Artificial neural networks are able to derive empirical models from a collection of experimental data. This applies in particular to complex, nonlinear relationships between input and output data. 

Optimisation and empirical modeling of hg-icp-aes anal5dical technique simplex and artificial neural networks were combined. 

Application of Artificial Neural Network AND Empirical Modeling in Yarn and Woven... 

The natural neural network is such an incredibly complex creation that it would be futile to even attempt to manufacture an exact copy. However, it is possible to create a biologically inspired empirical model containing many densely linked nonlinear processing units (called artificial neurons). The artificial neuron carries out the conversion (in general, nonlinear) of input vector U into output value Y (approximation of the representation being the basis of empirical models) in a manner similar to that of the brain neuron (Fig. 3.5). 

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