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VLSI Artificial Neural Networks Engineering

Engineers have long been fascinated by how efficient and how fast biological neural networks are capable of performing such complex tasks as recognition. Such networks are capable of recognizing input data from any of the five senses with the necessary accuracy and speed to allow living creatures to...

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Bibliographic Details
Other Authors: Elmasry, Mohamed I. (Editor)
Format: eBook
Language:English
Published: New York, NY Springer US 1994, 1994
Edition:1st ed. 1994
Subjects:
Complex Systems
Electrical And Electronic Engineering
Electrical Engineering
Electronic Circuits
System Theory
Mathematical Physics
Electronic Circuits And Systems
Theoretical, Mathematical And Computational Physics
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 7.2 Adaptive Probabilistic Neural Networks: APNN and ANNC
  • 7.3 Bayesian PDF Model Selection
  • 7.4 Maximum Likelihood and Maximum Mutual Information Train-ing
  • 7.5 Experimental Results
  • 7.6 The Adaptive Feature Extraction Nearest Neighbor Classifier “AFNN”
  • 7.7 Conclusions
  • 8 A Parallel ANN Architecture for Fuzzy Clustering
  • 8.1 Introduction
  • 8.2 Fuzzy Clustering and Neural Networks
  • 8.3 Fuzzy Competitive Learning Algorithm
  • 8.4 Mapping Algorithm Onto Architecture
  • 8.5 Fuzzy Clustering Neural Network (FCNN) Architecture: Processing Cells
  • 8.6 Comparison With The Fuzzy C-Mean (FCM) Algorithm
  • 8.7 Conclusions
  • 9 A Pipelined Ann Architecture for Speech Recognition
  • 9.1 Introduction
  • 9.2 Definition and Notation
  • 9.3 PNN Architecture: Processing Stages
  • 9.4 Case Studies
  • 9.5 Performance Analysis
  • 9.6 Conclusions
  • 1 An Overview
  • 1.1 Introduction
  • 1.2 Biological Neural Networks
  • 1.3 Artificial Neural Networks (ANNs)
  • 1.4 Artificial Neural Network Algorithms
  • 1.5 Supervised Neural Networks
  • 1.6 Unsupervised Neural Networks
  • 1.7 Neural Network Architectures and Implementations
  • 1.8 Book Overview
  • 2 A Sampled-Data CMOS VLSI Implementation of a Multi-Character ANN Recognition System
  • 2.1 Introduction
  • 2.2 ANN Implementation Techniques
  • 2.3 Developed CMOS Circuits for ANNs
  • 2.4 The Prototype MLP ANN Model Architecture
  • 2.5 MLP ANN Model Simulations
  • 2.6 ANN Circuit Simulations
  • 2.7 The Developed VLSI Architectures
  • 2.8 The Developed Two-Character ANN Recognizer
  • 2.9 The Proposed Multi-Character ANN Recognition System
  • 2.10 Conclusions
  • 3 A Design Automation Environment for Mixed Analog/Digital ANNs
  • 3.1 Introduction
  • 3.2 Mixed Analog/Digital ANN Hardware
  • 3.3 Overview of the Design Automation Environment
  • 3.4 Data Flow Graph
  • 3.5 The Analyzer
  • 3.6 The Design Library
  • 3.7 The Synthesizer
  • 3.8 Design Examples
  • 3.9 Conclusions
  • 4 A Compact VLSI Implementation of Neural Networks
  • 4.1 Introduction
  • 4.2 The Building Blocks
  • 4.3 The Circuit Implementation Example
  • 4.4 Expanding the Network with Multiple Chips
  • 4.5 Conclusions
  • 5 An All-Digital VLSI ANN
  • 5.1 Introduction
  • 5.2 Neocognitron Neural Network Model
  • 5.3 Digi-Neocognitron (DNC): A Digital Neural Network Model for VLSI
  • 5.4 Character Recognition Example
  • 5.5 Advantages for VLSI Implementation
  • 5.6 Conclusions
  • 6 A Neural Predictive Hidden Markov Model Architecture for Speech and Speaker Recognition
  • 6.1 Introduction
  • 6.2 Automatic Speech Recognition Methodologies
  • 6.3 An ANN Architecture for Predictive HMMs
  • 6.4 Discriminative Training of the Neural Predictive HMM
  • 6.5 Speaker Recognition Using the Neural Predictive HMM
  • 6.6 Conclusions
  • 7 Minimum Complexity Neural Networks for Classification
  • 7.1 Introduction

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