Analog VLSI on-chip learning Neural Networks represent a mature technology for a large
number of applications involving industrial as well as consumer appliances. This is …

Analog VLSI implementations of artificial neural networks are usually considered efficient for
the small area and the low power consumption they require, but very poor in terms of …

The architecture, the design, and the analog very large scale integration (VLSI)
implementation of a feature extractor chip for optical character recognition (OCR) systems …

In this paper we present the analog CMOS architecture of a Multi Layer Perceptron network
with on-chip stochastic Back Propagation learning. The learning algorithm is based on a …

This paper presents a signal processor based on neural computation rather than traditional
programming. This processor has analog input-output, analog processing unit and analog …

In this paper we present the analog architecture of a VLSI feature extraction module for a
neural network based optical character recognition system. The algorithm for features …

In this paper, analog very large scale integration complementary metal oxide semiconductor
(VLSI CMOS) circuits that implement different mathematical functions, equations, or …

The analog VLSI implementation of an on-chip learning neural network is discussed in this
paper. The multi layer perceptron paradigm and back propagation learning rule have been …

In this chapter, we introduce an analog chip hosting a self-learning neural network with local
learning rate adaptation. The neural architecture has been validated through intensive …

This paper, describing a computer graphics tool for clinical vision, is the result of an effort to
find out the potential of and possible improvements to a tool to help opticians. The software …