Sharif Digital Repository

This effort attempts to study the change point problem in the area of non-linear profiles. A method based on Artificial Neural Networks (ANN) is proposed for estimating the real time of a single step change. The feature vector of the proposed Multi-Layer Perceptron (MLP) is based on Z and control chart statistics for nonlinear profiles. The merits of the proposed estimator are evaluated through simulation experiments. The results show that the estimator provides an accurate estimate of the single step change point in non-linear profiles in the selected case problem. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of... 

A feed forward multi-layer perceptron neural network was developed to predict carbon dioxide loading capacity of chemical absorbents over wide ranges of temperature, pressure, and concentration based on the molecular weight of solution. To verify the suggested artificial neural network (ANN), regression analysis was conducted on the estimated and experimental values of CO2 solubility in various aqueous solutions. Furthermore, a comparison was performed between results of the proposed neural network and experimental data that were not previously used for network training, as well as a set of data for binary solutions. Comparison between the proposed multi-layer perceptron (MLP) network and... 

In this paper we present an adaptive method for image compression that is based on complexity level of the image. The basic compressor/de-compressor structure of this method is a multilayer perceptron artificial neural network. In adaptive approach different Back-Propagation artificial neural networks are used as compressor and de-compressor and this is done by dividing the image into blocks, computing the complexity of each block and then selecting one network for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability in complexity estimation... 

Nowadays, genetic disorders, like cancer and birth defects, are a great threat to human life. Since the first noticing of these types of diseases, many efforts have been made and researches performed in order to recognize them and find a cure for them. These disorders affect genes and they appear as abnormal traits in a genetic organism. In order to recognize abnormal genes, we need to predict splice sites in a DNA signal; then, we can process the genetic codes between two continuous splice sites and analyze the trait that it represents. In addition to abnormal genes and their consequent disorders, we can also identify other normal human traits like physical and mental features. So the... 

In this paper, Artificial Neural Networks (ANN) has been used to identify the dynamics of robots used in haptic and master slave devices in order to improve transparency. In haptic and master slave devices, transparency depends on some factors such as robot's mass and inertia, gravitational forces and friction [1]. In such systems, mass and inertia of the robot has an undesirable effect on the system outputs, which should be neutralized for improved transparency. The main purpose of this paper introducting a method to neutralize the undesirable effects of mass and inertia of the robot. A recurrent multilayer perceptron (RMLP) is used in a way that the inputs and outputs of the neural network... 

We propose a method for steganalysis of still, grayscale images using a novel set of features that are extracted from images. This feature set employs the Gabor filter coefficients to train a multi-layer perceptron neural network and a support vector machine classifier. We show that incorporation of the Gabor filter coefficients to the feature sets of images could have a significant role in discrimination between clean and altered images. Experimental results show that the proposed method outperforms previous methods, introduced for steganalysis of LSB-matching image steganography, in terms of both discrimination accuracy and feature set dimensionality. © 2009 IEEE  

Neural networks have been applied very successfully in the identification and control of dynamic systems. The universal approximation capabilities of the multilayer perceptron have made it a popular choice for modeling nonlinear systems and for implementing general-purpose nonlinear controllers. In this paper we try to model and control the mass-spring-damper mechanism as a 1 DOF system using neural networks. The control architecture used in this paper is Model reference controller (MRC) as one of the popular neural network control architectures  

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural, temporary, and iterative phenomenon that is caused by shortage in rainfall, which affects people's health and well-being adversely as well as impacting the society's economy and politics with far-reaching consequences. Information on intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, and therefore, lessen the risks associated with drought episodes. One of the major challenges of modeling drought (and short-term forecasting) in Iran is unavailability of long-term meteorological data for many parts of the... 

This study presents the utilisation of neural-network for bi-level image compression. In the proposed lossy compression method, the locations of pixels of image are applied to the inputs of a multilayer perceptron neural-network. The output of the network denotes the pixel intensity (0 or 1). The final weights of the trained neural-network are quantised, represented by a few bits, Huffman encoded and then stored as the compressed image. In the decompression phase, by applying the pixels locations to the trained network, the output determines the intensity. The results of experiments on more than 4000 different images indicate higher compression rate of the proposed structure compared with... 

In this work, an optimum multilayer perceptron neural network is developed to model the correlation between hot working parameters (temperature, strain rate and strain) and flow stress of IN625 alloy. Three variations of standard back propagation algorithm (Broyden, Fletcher, Goldfarb and Shanno quasi-Newton, Levenberg-Marquardt and Bayesian) are applied to train the model. The results show that, in this case, the best performance, minimum error and shortest converging time are achieved by the Levenberg-Marquardt training algorithm. Comparing the predicted values and the experimental values reveals that a well trained network is capable of accurately calculating the flow stress of the alloy...