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In the earlier work authors have shown that prior knowledge neural approach can be successfully applied for bias dependent noise modelling of microwave transistors, which is very applicable in the cases where there are no enough data to develop pure black-box neural models. In this paper the mentioned model is extended to a class of devices made in the same technology but differing in the gate width...
Target tracking is a well studied topic in wireless sensor networks. However, uncertainty existed in sensor networks presents new challenges for it. Besides the energy conservation of networks, target tracking has to deal with different kinds of uncertainty, such as the impreciseness of positioning systems, environment noise and limited sensitivity of sensors. In this paper, we study the problem of...
Rapid growth of computer network scales has made traffic matrix estimation essential in network management. It can be used in load balancing, traffic detecting and so on. Since traffic should be considered temporally and spatially, prediction is complicated. Tracking dynamic changes of traffic, reducing estimation errors and increasing robustness to noise are factors which should be considered in...
One of the most significant parameter in increasing the efficiency of MLP NN that utilizes the EBP algorithm for training network is convergence speed which different methods have been proposed for improving it. In this paper, we use a variable learning rate method for increasing the convergence speed of EBP algorithm, which its idea have come from a one way presented to improve the efficiency of...
The olfactory system detects volatile chemical compounds, known as odour molecules or odorants. Such odorants have a diverse chemical structure which in turn interact with the receptors of the olfactory system. The insect olfactory system provides a unique opportunity to directly measure the firing rates that are generated by the individual olfactory sensory neurons (OSNs) which have been stimulated...
In this paper recent progress on adaptive nonlinear active noise control is presented. Particular attention is paid to a new learning algorithm for recurrent neural networks based on Adjoint Extended Kalman Filter that is developed for nonlinear active noise control. The overall control structure for active noise control is constructed using two recurrent neural networks: the first neural network...
This paper proposes an approach to recognize handwritten Tamil Character recognition. Handwritten Tamil character recognition refers to the process of conversion of handwritten Tamil character into printed Tamil character. It is difficult to process handwritten characters due to the great variations in writing styles, different size and orientation angle of the characters. In the proposed system the...
This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data.
The Naming Games (NG) are typical agent-based models for agreement dynamics, peer pressure and herding in social networks, and protocol selection in autonomous ad-hoc sensor networks. They form a large class that includes the Voter models and many others. By introducing a rare Poisson noise term to the signaling protocol of the NG, the resulting Markov Chain model called Noisy Naming Game (NNG) is...
An adaptive neural control scheme based on a new observer applied to quadrotors Helicopter is proposed in this paper. This technique is realized by using two parallel feedforward Artificial Neural Networks (ANN) for each subsystem of the quadrotor. The first one estimates on line the equivalent control term and the second ANN generates observer's corrective term. The main purpose in our work is to...
In this paper, a simple, accurate, fast and reliable black-box modeling is presented for the noise characterization of a microwave transistor using GRNN. GRNN-based modeling is applied to a chosen microwave transistor VMMK 1225 with an optimized training data set and the results are given in details.
This paper presents the results of Internal Model Control (IMC) for InnoSAT attitude control based on Neural Network (NN). IMC is composed of an inverse model connected in series with the plant and a forward model connected in parallel with the plant. The controller is achieved by estimating the plant and then finding its inverse model of the InnoSAT plant using the NN. The control signal error is...
This paper presents an integration of S-Transform and Probabilistic Neural Network (PNN) technique for identifying the location of a switched capacitor causing a power quality problem. The transient caused by the capacitor switching is one of the important power quality (PQ) problems since it may adversely affect the system as well as sensitive loads. S-Transform has the ability to detect the disturbance...
Distributed Neural Networks (DNNs) are generally providing self-scaling property together with higher noise immunity for resistive-type neural networks. Continuous Valued Number System (CVNS) is a potential candidate to build the DNNs; however, implementation of a CVNS digit in its complete form needs a high resolution environment which is not practical. Truncation methods are applied to CVNS digits...
Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. This biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based...
This paper presents a novel unsupervised and parallel learning technique for data clustering that are polluted by noise using neural network approaches. The proposed approach is based on a self-organizing incremental neural network. The design of two-layer neural network enables this system to represent the topological structure of unsupervised on-line data, reports the reasonable number of clusters,...
We propose a new method for the blind separation of multiple binary signals from a single general nonlinear mixture. In addition to the usual independence assumption on the input signals our key hypothesis is the asymmetry of the source probabilities. This condition allows us to express the output probability distribution as a linear mixture of the sources. We then proceed to solve the problem using...
The delineation of P and T waves is important for the interpretation of ECG signals. We propose a Bayesian detection-estimation algorithm for simultaneous detection, delineation, and estimation of P and T waves. A block Gibbs sampler exploits the strong local dependencies in ECG signals by imposing block constraints on the P and T wave locations. The proposed algorithm is evaluated on the annotated...
Effects on human vary between uniform and time-varying traffic noise with the same equivalent sound level. Firstly, the subjective evaluation experiment of time-varying traffic noise is designed and implemented in this paper to get truthful data. Secondly, taking the gender, employment and mood factors into account, some conclusions about the effects of traffic noise with different time-varying characteristics...
Porosity and permeability are the two fundamental and crucial reservoir parameters which are often used in reservoir description. However, the two properties are difficult to be measured and predicted, due to some influences such as rock type and cement and so on. In this paper, we proposed a new method combined of fuzzy theory, principal component analysis and the neural network to predict the porosity...
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