The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were ...

This paper considers the similarity between two measures of air pollution/quality control, on the one hand, and widely used indicators of life quality and welfare, on the other. We have developed a multi-layer perceptron neural network system which is trained to predict the measurements of air quality (emissions of sulphur and nitrogen oxides), using Eurostat data for 34 countries. We used life...

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will conc...

Statistical mechanics is applied to estimate the maximal capacity per weight (a,) of a two-layer feed-forward network with discrete weights of depth 1, functioning as a parity machine of the K hidden units. For each K and lSZo(K'), the maximal theoretical capacity a, = 10g2(2Z) is achieved, the capacity per bit is 1, the average overlap between different solutions is zero and Zo(K) logK for lar...

Within forest growth modeling LOGIT models are used to predict individual tree mortality. In this paper we present, Multi-Layer Perceptron, Learning Vector Quantization and Cascade Correlation networks as different formalisms for mortality predictions. The data set for parameterizing the LOGIT model and training the different neural network types comes from the Austrian National Forest Inventor...

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stocks, or stock markets. In this paper, the StandardlkPoors 500 Index is modeled using different neural network classification architectures. Most previous experiments used multilayer perceptrons for stock market forecasting. In this paper, a multilayer perceptron architecture and ZL probabilistic neura...

In this paper a statistical point of view of feedforwared neural networks is presented. The hidden layer of a multilayer perceptrokneural network is identified of representing the mapping of random vectors. Utilizing hard limiter activation functions, the second and all further layers of the multilayer perceptron, including the output layer; represent the mapping of a boolean function. Boolean ...

This paper proposes an intelligent approach for dynamic identification of the vehicles. The proposed approach is based on the data-driven identification and uses a high-performance local model network (LMN) for estimation of the vehicle’s longitudinal velocity, lateral acceleration and yaw rate. The proposed LMN requires no pre-defined standard vehicle model and uses measurement data to identif...

This paper compares, for a microfinance institution, the performance of two individual classification models: Logistic Regression (Logit) and Multi-Layer Perceptron Neural Network (MLP), to evaluate the credit risk problem and discriminate good creditors from bad ones. Credit scoring systems are currently in common use by numerous financial institutions worldwide. However, credit scoring using ...