A nonlinear correlator detector for the detection of a signal class with some intra class variance is developed using the modified probabilistic neural network and the general regression neural network. An application, involving the detection of regular tone bursts transmitted over a poor and noisy radio channel subjected to fading, random noise and impulse noise effects, is used to show the effectiveness of the method as compared to a linear correlato
It is often assumed that interference or noise signals are Gaussian stochastic processes. Gaussian n...
The well-known method of detecting a useful signal in the presence of noise during underwater remote...
Noise or interference is often assumed to be a random process. Conventional linear filtering, contro...
This paper introduces a practical and very effective network for nonlinear signal processing called ...
The goal of this dissertation is to try to apply artificial intelligence algorithms to the field of...
Many communications and sensing applications hinge on the detection of a signal in a noisy, interfer...
A Probabilistic Neural Network (PNN) is proposed and applied here for implementation of a Maximum ...
The authors compare the efficiency of a classifier based on probabilistic neural networks and the ge...
A fault detection method for nonlinear systems, which is based on Probabilistic Neural Network Filte...
We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the ...
Application of Neural Network to signal detection in CDMA multi-user communications Gaussian channe...
Evoked potentials (EPs) are the special signals that are non-stationary and corrupted by relatively ...
For finding keys to understand and elucidate a phenomenon, it is essential to detect dependences amo...
By means of a backpropagation neural network a model has been built which is able to distinguish bet...
We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the ...
It is often assumed that interference or noise signals are Gaussian stochastic processes. Gaussian n...
The well-known method of detecting a useful signal in the presence of noise during underwater remote...
Noise or interference is often assumed to be a random process. Conventional linear filtering, contro...
This paper introduces a practical and very effective network for nonlinear signal processing called ...
The goal of this dissertation is to try to apply artificial intelligence algorithms to the field of...
Many communications and sensing applications hinge on the detection of a signal in a noisy, interfer...
A Probabilistic Neural Network (PNN) is proposed and applied here for implementation of a Maximum ...
The authors compare the efficiency of a classifier based on probabilistic neural networks and the ge...
A fault detection method for nonlinear systems, which is based on Probabilistic Neural Network Filte...
We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the ...
Application of Neural Network to signal detection in CDMA multi-user communications Gaussian channe...
Evoked potentials (EPs) are the special signals that are non-stationary and corrupted by relatively ...
For finding keys to understand and elucidate a phenomenon, it is essential to detect dependences amo...
By means of a backpropagation neural network a model has been built which is able to distinguish bet...
We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the ...
It is often assumed that interference or noise signals are Gaussian stochastic processes. Gaussian n...
The well-known method of detecting a useful signal in the presence of noise during underwater remote...
Noise or interference is often assumed to be a random process. Conventional linear filtering, contro...