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Hypothyroidism in infants is caused by insufficient production of hormones by the thyroid gland. Due to stress in the chest cavity due to the enlarged liver, the cry signals are unique and can be distinguished from healthy infant cries. We investigate the usage of the Multilayer Perceptron (MLP) classifier to diagnose infant hypothyroidism. The Mel Frequency Cepstrum Coefficients (MFCC) feature extraction...
The novel Imperialist Competitive Algorithm (ICA) that was recently introduced has a good performance in some optimization problems. The ICA inspired by sociopolitical process of imperialistic competition of human being in the real world. In this paper, a new Adaptive Imperialist Competitive Algorithm (AICA) is proposed. In the proposed algorithm, for an effective search, the Absorption Policy changed...
Neural Networks are powerful tools for function approximation problems. A possible peculiar application of neural networks is that proposed here: estimating the univariate mean of a distribution from a finite sample. This problem characterizes a huge number of applicative and scientific problems. The Gaussian distribution case is analyzed, however the proposed analysis is of general validity and can...
Smart sensing of environmental parameters is an important task in robotics, process industries, sensor networks and autonomous systems. In this paper, we propose a novel Chebyshev neural network (ChNN) to develop smart sensors which can provide linearized and accurate readout, and can compensate for nonlinear environmental disturbances including additive noise. By taking two environmental models and...
The subjective evaluation of marbles based on their visual appearance could be replaced by an automated texture classification system, intending to achieve high classification accuracy and production effectiveness. The existing marble classification methods from a computational point of view are either too complex or very expensive. Nowadays some inspection systems in marble industry that automates...
Multilayer feed-forward neural network is widely used based on minimization of an error function. Back propagation is a famous training method used in the multilayer networks but it often suffers from the problems of local minima and slow convergence. These problems take place due to the gradient behavior of mostly used sigmoid activation function (SAF). Weight update becomes zero when activation...
Few systems have been developed for the detection of fatigue / stress level of a vehicular driver in order to monitor and control the alertness level for preventing road accidents. Physiological parameters of the body vary with respect to every minute variation in mental and physical states and this work utilizes select such parameters. The present work is a part of the BITS Life-Guard research project...
In this paper we used a generalized net which gives a possibility for parallel optimization of multilayer neural networks. For training the backpropagation algorithm with momentum was considered. We proposed a generalized net model of parallel training of two neural networks with different architectures. The difference between the networks is in the number of neurons in main difference of the neural...
Summary form only given. The lecture starts with the discussion of the methodology of Fault Detection and Isolation (FDI) for dynamic systems. Then recent model-based approaches to FDI analytical ones and those based on soft computing are surveyed. Taking into account many limitations of analytical methods, the main attention is focused on the use of neural networks in FDI for solving specific tasks...
Neural networks algorithms have already shown good capabilities in handling nonlinear inversion problems in hyperspectral remote sensing. In this study we investigate on their potential in solving spectral unmixing. A Multi-Layer Perceptron (MLP) neural network scheme is trained for the implementation of a pixel-based classification algorithm. Subsequently, for the output response, the “winner-takes-all”...
Hypothyroidism results from an insufficiency either in the production or/and in the action of the thyroid hormones. Most of the patients in hypothyroidism state must adopt hormonal replacement therapy for all their lives. An intelligent controller model for supplying therapeutic drugs to primary hypothyroidism patients, without the thyroid gland, is investigated in this work. Two multilayer perceptron...
Power transformer is one of the most important components in electrical network which play effective role in the electrification. The same way that continuous performance of transformers is necessary to retaining the network reliability, forecasting its costs is also important for manufacturer and industrial companies. Since major amount of transformers costs is related to its raw materials, so having...
Artificial neural network (ANN) - as general tools for implementing nonlinear mapping between inputs and outputs - is proved to be feasible position estimation for self-sensing active magnetic bearings. A neural network with five neurons in hidden layer is constructed and well trained. With neural network act as position feedback, the active magnetic bearings system performed well. Simulation results...
The focus of this article is to select the best architecture for a Neural Network Energy Prediction Model (NNEPM). A few network architecture is simulated and modeled; Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Radial Basis Function (GRBF), and Elman Network (Elman). From these networks, the network performances are compared and the best architecture is chosen for the NNEPM...
Protection in power system is very important to ensure the systems are in a good condition without any failure. It is necessary that the protection system can operate at the shortest time to clear the fault as soon as possible. Overcurrent relay protection is depending on their time-current characteristic curve. In this particular characteristic curve, the required relay operating time can be determined...
In this paper, we introduce an artificial neural network (ANN) based motion control methodology of micro actuators for microelectromechanical systems (MEMS). The control strategy is based on a multilayer perception (MLP) trained online using a Lyapunov-based learning technique. The controller achieves high precision tracking under unknown system dynamics including hysteresis and external disturbance...
In this paper we deal with the problem of user-driven Call Admission Control for Voice over IP communications in a Wireless LAN environment. We argue that state-of-the-art solutions to this problem are suboptimal, since they leverage on analytical models whose assumptions are not necessarily verified in the scenario considered. To overcome this problem, we propose a cognitive solution based on Multilayer...
Microbially assisted recovery of copper from low-grade chalcopyrite has been reported to be a very difficult process, conventional hydrometallurgical methods were limited by many parameters. This study focus on the design and the training of a Multi-Layer Perceptron classifier for the optimized preparation conditions for bioleaching of chalcopyrite. The proposed approach uses the heuristic Backpropagation...
The objective of this work is to investigate the predictability of the hairiness of the cotton yarn from a cone winding machine using a multilayered perception (MLP) feed -forward back-propagation network in an artificial neural network system. A five-quality index (feeder distance, winding speed, thread cleaner gauge, tension washer weight, and rupture ring highness) and cotton yarn hairiness of...
In this paper, a novel neutral-network-based model is proposed to describe an X-Y macro-positioning stage. As the friction exists in the stage, the stage shows some complex behavior due to the non-smooth characteristic of the friction. In order to describe the non-smooth behavior of the stage, in this model, a non-smooth active function is proposed to construct the hidden neurons. Then, a training...
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