Add to ORKGVarious types of neural networks ma! be used in multi-dimensional classification tasks; among them, Bayesian and LVQ algorithms are interesting respectively for their performances and their simplicity of operations, The large number of operations involved in such algorithms may however be incompatible with on-line applications or with the necessity of portable small size systems. This paper describes a neural network classifier system based on a fully analog operative chip coupled with a digital control system, The chip implements sub-optimal Bayesian classifier and LVQ algorithms
This paper describes the implementation of a partially connected neural network using FPGAs (Field P...
Analog VLSI implementations of artificial neural networks are usually considered efficient for the s...
<div>With the advent of new technologies and advancement in medical science we are trying to process...
Many neural-like algorithms currently under study support classification tasks. Several of these alg...
Kernel-based classifiers are neural networks (radial basis functions) where the probability densitie...
This paper presents a digital VLSI implementation of a feedforward neural network classifier based o...
Kernel-based classifiers are neural networks (radial basis functions) where the probability densitie...
This paper describes how to implement a partially connected neural network by Giga-Ops Spectrum G800...
This paper presents a digital VLSI implementation of a feed-forward neural network classifier based ...
A special purpose neural IC is described which will be utilised in a data-acquisition system in DESY...
Analog VLSI on-chip learning Neural Networks represent a mature technology for a large number of app...
For the last two decades, lot of research has been done on neural networks, resulting in many types ...
This paper describes a new evolvable hardware organization and its learning algorithm to generate bi...
The design, implementation and operation of a low power multilayer perceptron chip (Kakadu) in the f...
Analog VLSI implementations of artificial neural networks are usually considered efficient for the s...
This paper describes the implementation of a partially connected neural network using FPGAs (Field P...
Analog VLSI implementations of artificial neural networks are usually considered efficient for the s...
<div>With the advent of new technologies and advancement in medical science we are trying to process...
Many neural-like algorithms currently under study support classification tasks. Several of these alg...
Kernel-based classifiers are neural networks (radial basis functions) where the probability densitie...
This paper presents a digital VLSI implementation of a feedforward neural network classifier based o...
Kernel-based classifiers are neural networks (radial basis functions) where the probability densitie...
This paper describes how to implement a partially connected neural network by Giga-Ops Spectrum G800...
This paper presents a digital VLSI implementation of a feed-forward neural network classifier based ...
A special purpose neural IC is described which will be utilised in a data-acquisition system in DESY...
Analog VLSI on-chip learning Neural Networks represent a mature technology for a large number of app...
For the last two decades, lot of research has been done on neural networks, resulting in many types ...
This paper describes a new evolvable hardware organization and its learning algorithm to generate bi...
The design, implementation and operation of a low power multilayer perceptron chip (Kakadu) in the f...
Analog VLSI implementations of artificial neural networks are usually considered efficient for the s...
This paper describes the implementation of a partially connected neural network using FPGAs (Field P...
Analog VLSI implementations of artificial neural networks are usually considered efficient for the s...
<div>With the advent of new technologies and advancement in medical science we are trying to process...