Applications of artificial neural networks pdf

Applications of Artificial Neural Networks for Nonlinear Data is a collection of innovative research on the contemporary nature of artificial neural networks and their specific implementations within data analysis. While highlighting topics including propagation functions, optimization techniques, and learning methodologies, this book is ideally designed for researchers, statisticians, academicians, developers, scientists, practitioners, students, and educators seeking current research on the use of artificial neural networks in diagnosing and solving nonparametric problems.

International Journal of Remote Sensing, 36 6. Kumar, P. Author : W. Alam, J. These HONNs can produce classifiers IGI Global. Majumder, S. A feed-forward network was used for the map learning. The learning of neural networks Its advantages are in the ability to handle with nonlinear data , highly correlated variables and the potential for Therefore, to apply ANN for nonlinear filtering, one must be able to collect an extensive set of training The next section provides a practical introductory guide for designing a neural network model.

Variable selection The input variables important for modeling variable s under study are selected by suitable variable selection procedures. Formation of training, testing and validation sets The data set is divided into three distinct sets called training, testing and validation sets. The training set is the largest set and is used by neural network to learn patterns present in the data.

The testing set is used to evaluate the generalization ability of a supposedly trained network. A final check on the performance of the trained network is made using validation set. Neural network architecture Neural network architecture defines its structure including number of hidden layers, number of hidden nodes and number of output nodes etc.

In theory, a neural network with one hidden layer with a sufficient number of hidden neurons is capable of approximating any continuous function. Number of hidden nodes: There is no magic formula for selecting the optimum number of hidden neurons. However, some thumb rules are available for calculating number of hidden neurons.

A rough approximation can be obtained by the geometric pyramid rule proposed by Masters Activation function: Activation functions are mathematical formulae that determine the output of a processing node. Each unit takes its net input and applies an activation function to it. Non linear functions have been used as activation functions such as logistic, tanh etc. Transfer functions such as sigmoid are commonly used because they are nonlinear and continuously differentiable which are desirable for network learning.

Evaluation criteria The most common error function minimized in neural networks is the sum of squared errors. Other error functions offered by different software include least absolute deviations, least fourth powers, asymmetric least squares and percentage differences.

Neural network training Training a neural network to learn patterns in the data involves iteratively presenting it with examples of the correct known answers. The objective of training is to find the set of weights between the neurons that determine the global minimum of error function.

This involves decision regarding the number of iteration i. Conclusion The computing world has a lot to gain from neural networks. A large number of claims have been made about the modeling capabilities of neural networks, some exaggerated and some justified.

Hence, to best utilize ANNs for different problems, it is essential to understand the potential as well as limitations of neural networks. For some tasks, neural networks will never replace conventional methods, but for a growing list of applications, the neural architecture will provide either an alternative or a complement to these existing techniques. Finally, I would like to state that even though neural networks have a huge potential we will only get the best of them when they are integrated with Artificial Intelligence, Fuzzy Logic and related subjects.

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A comprehensive review for industrial applicability of artificial neural networks By Marcelo Simoes. Download PDF.