The Hopfield neuron with predictive hysteresis is proposed and the efficiency of employing these neurons in analog to digital conversion, via the Hopfield Neural Network, will be demonstrated. Traditional hysteresis is defined, generally, as the occurrence of a delayed effect when forces acting on an object are varied. It will be shown that predictive hysteresis, a type of reverse hysteresis, improves upon the speed of hysteretic and non hysteretic systems without compromising the accuracy
Fifth-generation telecommunications networks are expected to have technical requirements which far o...
Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis ha...
Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circui...
The proteretic Hopfield neural network is studied in this work. Hysteresis is a Greek word which mea...
In this paper we compare analog to,digital conversion (ADC) delay in Hopfield ADC and asymmetrical (...
In this paper we study the problem of designing a neural network that gives the correct binary repre...
In this chapter, we present an overview of the recent advances in analog-to-digital converter (ADC) ...
A model of neurons with CHN (Continuous Hysteresis Neurons) for the Hopfield neural networks is stud...
It is known that an analog Hopfield neural network with time delay can generate the outputs which ar...
In this paper, we propose a continuous hysteresis neurons (CHN) Hopfield neural network architecture...
This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog...
Neural Network (NN) and actual frequency transplantation (AFT) are combined for prediction of dynami...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper deals with an improved neural based analog to digital converter. New architecture is prop...
This paper introduces the definition,principle,model and basic learning rules of feedback neural net...
Fifth-generation telecommunications networks are expected to have technical requirements which far o...
Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis ha...
Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circui...
The proteretic Hopfield neural network is studied in this work. Hysteresis is a Greek word which mea...
In this paper we compare analog to,digital conversion (ADC) delay in Hopfield ADC and asymmetrical (...
In this paper we study the problem of designing a neural network that gives the correct binary repre...
In this chapter, we present an overview of the recent advances in analog-to-digital converter (ADC) ...
A model of neurons with CHN (Continuous Hysteresis Neurons) for the Hopfield neural networks is stud...
It is known that an analog Hopfield neural network with time delay can generate the outputs which ar...
In this paper, we propose a continuous hysteresis neurons (CHN) Hopfield neural network architecture...
This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog...
Neural Network (NN) and actual frequency transplantation (AFT) are combined for prediction of dynami...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper deals with an improved neural based analog to digital converter. New architecture is prop...
This paper introduces the definition,principle,model and basic learning rules of feedback neural net...
Fifth-generation telecommunications networks are expected to have technical requirements which far o...
Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis ha...
Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circui...