Greenhouses are classified as complex systems, so it is difficult to implement classical control methods for this kind of process. In our case we have chosen neural network techniques to drive the internal climate of a greenhouse. An Elman neural network has been used to emulate the direct dynamics of the greenhouse. Based on this model, a multilayer feedforward neural network has been trained ...

Feed-forward deep neural networks have been used extensively in various machine learning applications. Developing a precise understanding of the underling behavior of neural networks is crucial for their efficient deployment. In this paper, we use an information theoretic approach to study the flow of information in a neural network and to determine how entropy of information changes between co...

The paper presents a new algorithm to build a feedforward neural network with a single hidden layer. The algorithm starts with 1 hidden unit and the new hidden units are added to the network only if they improve the classification accuracy of the network on the cross-validation samples. The initialization of the weights and bias is done using Nguyen-Widrow method and the network is trained with...

Neural networks have been applied to an entire plethora of learning tasks, such as text recognition, credit rating analysis and prediction, and so forth. Surprisingly, there have never been proposals suggesting their usage for collaborative filtering (CF) scenarios. The objective of this diploma/master’s thesis is to close that gap by virtue of (i) conceiving and applying neural network models ...

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Pruning algorit hms for feed-forward neur al networks typically have the undesirable side effect of int erfering with t he learning pro cedure. The network reduct ion algorithm presented in this pap er is implemented by considering only directions in weight space that are orthogonal to those required by t he learning algorit hm. In this way, the network redu ction algorithm chooses a minimal ne...

Recurrent neural network (RNN) are being extensively used over feed-forward neural networks (FFNN) because of their inherent capability to capture temporal relationships that exist in the sequential data such as speech. This aspect of RNN is advantageous especially when there is no a priori knowledge about the temporal correlations within the data. However, RNNs require large amount of data to ...

We extend here a general mathematical model for feed-forward neural networks. Such a network is represented as a vectorial function f of two variables, x (the input of the network) and w (the weight vector). We have already shown that the differential of f can be computed with an extended back-propagation algorithm as well as with a direct method. In this paper, we show that the second differen...

The artificial neural networks, the learning algorithms and mathematical models mimicking the information processing ability of human brain can be used non-linear and complex data. The aim of this study was to predict the breeding values for milk production trait in Iranian Holstein cows applying artificial neural networks. Data on 35167 Iranian Holstein cows recorded between 1998 to 2009 were ...