Artificial neural networks prediction capabilities

Innovative multivariate statistical analyses, such as artificial neural network (ANN) and genetic algorithms (GAs), were also used in order build regression models with real predictive capability and applicable to unknown samples. 

Optimization can be simplified by employing the predictive capabilities of an artificial neural network (ANN). This multivariate approach has been shown to require minimal number of experiments that allow construction of an accurate experimental response surface (5, 6). The apposite model created from an experimental design should effectively relate the experimental parameters to the output values, which can be used to create an ANN with a strong predictive capacity for any conditions defined within the experimental space (4). 

To build a system capable of predicting characteristics of interest from measured values, we need to form a model which relates the measurements to the physical effect of interest. This model can then be applied to future measurements to predict the effect. In mathematical terms, the objective is to find the multidimensional function,/, such that y =f x), where jc is a new multivariate reading and y is the effect to be predicted. For the present, we shall assume that / is a linear function, for simplicity. Artificial neural networks (ANNs), a non-linear method, will be discussed later. 

A basic information about Artificial Neural Networks (ANNs) and their applications was introduced. A special attention was given to description of dynamic processes by mean of ANN. The drying kinetics of agricultural products are presented in the paper. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) network types are proposed for predicting changes of moisture content and temperature of material in during drying in the vibrofluidized bed. Capability of prediction of Artificial Neural Networks is evaluated in feed forward and recurrent structures. 

MacKay s textbook [114] offers not only a comprehensive coverage of Shannon s theory of information but also probabilistic data modeling and the mathematical theory of neural networks. Artificial NN can be applied when problems appear with processing and analyzing the data, with their prediction and classification (data mining). The wide range of applications of NN also comprises optimization issues. The information-theoretic capabilities of some neural network algorithms are examined and neural networks are motivated as statistical models [114]. 

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