in this paper, the grouting ability of sandy soils is investigated by artificial neural networks based on the results of chemical grout injection tests. in order to evaluate the soil grouting potential, experimental samples were prepared and then injected. the sand samples with three different particle sizes (medium, fine, and silty) and three relative densities (%30, %50, and %90) were injecte...

This paper presents artificial neural network (ANN)-based models for forecasting precipitation, in which the training parameters are adjusted using a parameter automatic calibration (PAC) approach. A classical ANN-based model, the multilayer perceptron (MLP) neural network, was used to verify the utility of the proposed ANN–PAC approach. The MLP-based ANN used the learning rate, momentum, and n...

This paper presents a (semi-)automatic processing technique for GPR data analysis. Exploiting the generalization capabilities of artificial neural networks (ANN), it will be shown that it is possible to feed a Multi-Layer Perceptron (MLP) with a suitable set of input features in order to determine the permittivity of a ground layer. A detailed performance assessment have proven that the algorit...

there is no accurate data for the bank cards fraud in iran. but, it seems to be a growing trend in this regard and in the near future it is going to become one of the critical problems in iran's banking system. unfortunately, not enough research works have been done in this field in our country and the banking system requires models that are efficient enough to ensure safe use of bank card...

Artificial Neural Network (ANN)-based diagnosis of medical diseases has been taken into great consideration in recent years. In this paper, two types of ANNs are used to classify effective diagnosis of Parkinson's disease. Multi-Layer Perceptron (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) ANNs were used to differentiate between clinical variables of sampl...

The use of local phoneme posterior probabilities has been increasingly explored for improving speech recognition systems. Hybrid hidden Markov model / artificial neural network (HMM/ANN) and Tandem are the most successful examples of such systems. In this thesis, we present a principled framework for enhancing the estimation of local posteriors, by integrating phonetic and lexical knowledge, as...

In a particular case of behavioural model reduction by ANNs, a validity domain shortening has been found. In mechanics, as in other domains, the notion of validity domain allows the engineer to choose a valid model for a particular analysis or simulation. In the study of mechanical behaviour for a cantilever beam (using linear and non-linear models), Multi-Layer Perceptron (MLP) Backpropagation...

Nowadays, one of the main issues to create challenges in medicine sciences by developing technology is the disease diagnosis with high accuracy. In the recent decades, Artificial Neural Networks (ANNs) are considered as the best solutions to achieve this goal and involve in widespread researches to diagnose the diseases. In this paper, we consider a Multi-layer Perceptron (MLP) ANN using back p...

The change of land use/land cover has been known as an imperative force in environmental alteration, especially in arid and semi-arid areas. This research was mainly aimed to assess the validity of two major types of land change modeling techniques via a three dimensional approach in Birjand urban watershed located in an arid climatic region of Iran. Thus, a Markovian approach based on two suit...

Due to various seasonal and monthly changes in electricity consumption, it is difficult to model it with conventional methods. This paper illustrates an Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network for household electricity consumption forecasting. This is the first study which uses MLP for forecasting household electricity consumption. Previ...