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Produktbild: VLSI Artificial Neural Networks Engineering
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VLSI Artificial Neural Networks Engineering

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

30.11.1994

Abbildungen

XV, 329 p.

Herausgeber

Mohamed I. Elmasry

Verlag

Springer Us

Seitenzahl

329

Maße (L/B/H)

23,4/15,6/2,1 cm

Gewicht

662 g

Auflage

1994 edition

Sprache

Englisch

ISBN

978-0-7923-9493-8

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

30.11.1994

Abbildungen

XV, 329 p.

Herausgeber

Mohamed I. Elmasry

Verlag

Springer Us

Seitenzahl

329

Maße (L/B/H)

23,4/15,6/2,1 cm

Gewicht

662 g

Auflage

1994 edition

Sprache

Englisch

ISBN

978-0-7923-9493-8

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: VLSI Artificial Neural Networks Engineering
  • 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.- 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.