Tag Archives: Artificial Neural Networks

Comparing the Technologies Used For the Application of Artificial Neural Networks (Published)

In the past few years, artificial neural networks (ANNs) have become a practice wherever it is necessary to solve problems of forecasting, classification, or control. ANNs are intuitively attractive because they are based on a primitive biological model of nervous systems. In the future, the development of such neurobiological models may lead to the creation of truly thinking computers. The areas of application of neural networks are very diverse – these are text and speech recognition, semantic search, expert systems, and decision support systems, prediction of stock prices, security systems, text analysis, etc. Based on the wide application of artificial neural networks, the application of numerous and diverse technologies can be inferred. In this research paper, a comparative approach towards the above-mentioned technologies will be undertaken in order to identify the optimal technology for each problem area in accordance with their respective advantages and disadvantages.  

Keywords: Applications, Artificial Neural Networks, Modules

A Suggested Approach to Artificial Neural Networks Modeling of Time Series (Published)

This research proposed a method of selecting an optimal combination of numbers of input (number of lagged values in the model) and of hidden nodes for modeling seasonal data using Artificial Neural Networks (ANN). Three data sets (rainfall, relative humidity and solar radiation) were used in assessing the proposed procedure and the resulting ANN models were compared with two traditional models (Holt-Winter’s and SARIMA). Models with large number of lagged values have shown tendency to outperform those with small number of lagged values. Selected ANN model was found to outperform the two traditional models on rainfall data; it performed better than SARIMA but worse than Holt-Winter’s model on relative humidity data and performed worse than the two methods on solar radiation data. The proposed procedure has hence, performed fairly well. Oscillatory performance recorded by ANN models that resulted from the proposed procedure in relation to the other two models only attests to the fact that no particular model is best on every data set. Rather than insist on elegance or sophistication, researchers should be guided by parsimony.

Keywords: Artificial Neural Networks, Forecasting, SARIMA, holt-winter, seasonal data

Application of Neural Networks in Weather Forecasting (Published)

Weather Forecasting is the task of determining future state of the atmosphere. Accurate weather forecasting is very important because agricultural and industrial sector largely depend on it. Weather forecasting has become an important field of research in the last few decades. In most of the cases the researcher had attempted to establish a linear relationship between the input weather data and the corresponding target data. The Neural Networks package supports different types of training or learning algorithms. In this paper, the application of neural networks to  study the design of neural network technique  for Kanyakumary District,Tamil Nadu, India. A total of ten years of data collected for training the net work. The network is trained using the Back propagation Algorithm, Radial Basis Function, Regression Neural Network, Optical Neural Network, and Fuzzy ARTMAP Neural Network. The Fuzzy ARTMAP  network can give the best overall results in terms of accuracy and training time. It is better correlated compared to the BPN,RBFN,GRNN and ONN networks. The proposed Fuzzy ARTMAP neural  network can also overcome several limitations such as a highly non-linear weight update and the slow convergence rate. 

Keywords: Artificial Neural Networks, Back Propagation, Forecasting, Fuzzy ARTMAP, Neural network, Optical Neural Network, Radial Basis Function, Regression Neural Network, Weather