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Neural networks (NNs) have proven to be a very powerful tool both for one-dimensional (1D) and two-dimensional (2D) direction of arrival (DOA) estimation. By avoiding complex and time-consuming mathematical calculations, NNs estimate DOAs almost instantaneously. This feature makes them very convenient for real-time applications. Further, unlike the well known MUSIC algorithm, neural network-based...
Empirical Artificial Neural Network (ANN) models are developed for Two-Dimensional Direction of Arrival (2D DOA) estimation of a source signal. For that purpose, experimental data obtained from measurements in an anechoic chamber are utilized. Performance of ANN models are compared to 2D MUSIC algorithm in regard to estimation accuracy and speed of calculations. It is demonstrated that the proposed...
The aim of this paper is to analyze and compare two artificial neural network based approaches for parameter extractions of microwave transistor equivalent circuits including noise. In the first approach equivalent circuit parameters are determined from the operating conditions, whereas in the second approach equivalent circuit parameters are determined directly from the measured scattering and noise...
In this paper, the generalized profile function models, GPFMs, based on linear regression and neural networks, are compared. GPFM provides an approximation of individual models (models of individual stem profile) facility using only two basic measurements. GPFM based on neural network is obtained as the average of all available normalized individual models. It is shown that the application of neural...
In this paper the solution of inverse kinematics problem and positioning of the industrial manipulator (ROBED03) with five degrees of freedom are presented. The algorithm is based on combination of Artificial Neural Networks (ANN) and Genetic Algorithm (GA).ANN was used for rough positioning providing the inputs for GA which performs precise adjustment. The algorithm was successfully tested in robot's...
The paper deals with a rapidly growing trend in robotics - anthropomimetics. Following a human paragon, bio-inspired control of the robot arm is presented using artificial neural networks. This work demonstrates results achieved by feedforward control comparing feedforward backpropagation networks and radial bases networks. Use of radial bases network prevails as an efficient tool to evade the exact...
This paper discusses solutions for generating melodies in the context of a system that intends to produce music with a specified emotional content. The research sets up from an existing system, which produces music by manipulating and combining MIDI music. We aim to analize the benefit from using automatic music composition techniques in order to improve musical quality, while conforming with the...
Researchers in the field of Neuromorphic Engineering are looking at ways to reduce the chip space required to mimic the huge processing capacity of the human brain and to simplify algorithms to train it. Since the recent fabrication of a memristor by the Hewlett Packard Company, there is a possibility to achieve both of these. With their crucial hysteresis properties, memristors can store charge during...
The paper proposed an approach for the optimization of the water flow algorithm for the text-line segmentation. Original method assumed the hypothetical water that flows to the document image frame from left to right and vice versa. It used the water flow angle as the only parameter. Algorithm's extended version introduced a water flow function, which is given as the power function. It exploited two...
The paper presents a further improvement of the adaptive retraining procedure of Artificial Neural Networks (ANNs) used for time series predictions. An important advantage of this approach is that the model is periodically adapted to the changes of the non-stationary environment. The retraining starts from proportionally reduced values of the parameters used in the previous version of the ANN model...
Complex-Valued Neural Networks are extensions of the classical Neural Networks. They have complex-valued weights, accept complex inputs and have more computational power than the classical ones. We discuss in this paper the training for Phase-Based Neurons, neural processing elements similar to Universal Binary Neurons, that uses as weights and bias complex numbers with unit magnitude, the phase being...
Authorship attribution, namely determination of the author of a text, may become an extraordinarily complex and sensitive job due to its relatively difficult feature extraction phase and highly nonlinear nature. This paper proposes a classification tool using committee machines consisting of multilayered perceptron neural networks (MLP) to identify the author of a text. Each expert is an individual...
The prediction of respiration-induced organ motion is crucial in some applications such as dynamic delivery of radiation dose. In this paper, we have proposed the novel approach to construct an acceleration-enhanced (AE) filter that is comprised of two independent adaptive channels. The filters use the adapted position and adapted acceleration, together with a weight factor to provide prediction for...
The paper tries to propose objectively existing hierarchy of control levels in Central Nervous System(CNS), based on already accomplished medical research results on Homo sapiens, i. e. Homo sapiens - sapiens CNS as well as the theory of control and the science of neural networks. Human CNS has been vague until 60 years ago. Although, there were intensive researches in the last 30 years worldwide,...
One step ahead prediction of peak electricity loads based on artificial neural networks (ANN) is presented. Two architectures of ANNs were implemented to produce predictions that were used to generate the final value as an average. The time instants when daily peak loads occur are produced simultaneously. Examples will be given confirming both the feasibility of the method and the need for further...
The Static state estimation is widely used in power systems for real time monitoring and analysis. Standard methods, such as the weighted least squares (WLS) algorithm, require the computation of bus admittance and Jacobian matrices and the solution is found in an iterative process. This paper presents an alternative for the classic state estimation (SE) algorithms, which uses a multilayer perceptron...
Electrical transformers are the most important elements in the process of transmission and distribution of electricity. Depending on the size and position of the transformer, the sudden device failure can cause tremendous damage. Neural networks are widespread technique for transformer health monitoring. Neural Network Ensembles are an advanced neural technique that improves the accuracy and reliability...
The generation of a small signal dynamic model of a solar cell was investigated. As a starting structure the usual one diode large signal dynamic model was used with known parameter values. A simple parallel linear RC circuit was used to represent the model while the element values were put to be functions of the illumination here represented by the photo-current. The element value versus photocurrent...
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