The modified probabilistic neural network for nonlinear time series analysis was developed and introduced in 1991. It effectively represents a simple family of clustering methods for reducing the size of Specht's general regression neural network and retaining all its benefits. Three hardware implementation schemes for the most basic form of the modified probabilistic neural network are described. The first is an optoelectronic implementation and the other two are very large scale integration designs: a virtual implementation and a fully parallel implementation
A nonlinear correlator detector for the detection of a signal class with some intra class variance i...
Neural networks and clustering are two of the many machine learning algorithms used for artificial i...
The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly ...
This paper addresses a simple way for neural network hardware implementation based on probabilistic ...
This paper introduces a practical and very effective network for nonlinear signal processing called ...
In this paper, we present three different methods for implementing the Probabilistic Neural Network ...
[eng] This paper presents a new methodology for the hardware implementation of neural networks (NNs)...
A physical implementation of a non-volatile resistive switching device (ReRAM) and linking its conce...
A novel adaptive technique is proposed for the complex-valued modified probabilistic neural network ...
Feedforward neural networks are massively parallel computing structures that have the capability of ...
Conference of 2013 13th IEEE International Conference on Nanotechnology, IEEE-NANO 2013 ; Conference...
It was pointed out in this paper that the planar topology of current backpropagation neural network ...
Recent developments in microelectronic technology has diverted the interest of researchers towards h...
In recent years, hardware implementation of neural networks has received increasing attention from r...
Based on the idea of an important cluster, a new multi-level probabilistic neural network (MLPNN) is...
A nonlinear correlator detector for the detection of a signal class with some intra class variance i...
Neural networks and clustering are two of the many machine learning algorithms used for artificial i...
The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly ...
This paper addresses a simple way for neural network hardware implementation based on probabilistic ...
This paper introduces a practical and very effective network for nonlinear signal processing called ...
In this paper, we present three different methods for implementing the Probabilistic Neural Network ...
[eng] This paper presents a new methodology for the hardware implementation of neural networks (NNs)...
A physical implementation of a non-volatile resistive switching device (ReRAM) and linking its conce...
A novel adaptive technique is proposed for the complex-valued modified probabilistic neural network ...
Feedforward neural networks are massively parallel computing structures that have the capability of ...
Conference of 2013 13th IEEE International Conference on Nanotechnology, IEEE-NANO 2013 ; Conference...
It was pointed out in this paper that the planar topology of current backpropagation neural network ...
Recent developments in microelectronic technology has diverted the interest of researchers towards h...
In recent years, hardware implementation of neural networks has received increasing attention from r...
Based on the idea of an important cluster, a new multi-level probabilistic neural network (MLPNN) is...
A nonlinear correlator detector for the detection of a signal class with some intra class variance i...
Neural networks and clustering are two of the many machine learning algorithms used for artificial i...
The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly ...