Neural-network algorithms and architectures for pattern classification

Miscellaneous · Tue Jan 01 00:00:00 EST 1991

OSTI ID:5521602

Mao, Weidong

The study of the artificial neural networks is an integrated research field that involves the disciplines of applied mathematics, physics, neurobiology, computer science, information, control, parallel processing and VLSI. This dissertation deals with a number of topics from a broad spectrum of neural network research in models, algorithms, applications and VLSI architectures. Specifically, this dissertation is aimed at studying neural network algorithms and architectures for pattern classification tasks. The work presented in this dissertation has a wide range of applications including speech recognition, image recognition, and high level knowledge processing. Supervised neural networks, such as the back-propagation network, can be used for classification tasks as the result of approximating an input/output mapping. They are the approximation-based classifiers. The original gradient descent back propagation learning algorithm exhibits slow convergence speed. Fast algorithms such as the conjugate gradient and quasi-Newton algorithms can be adopted. The main emphasis on neural network classifiers in this dissertation is the competition-based classifiers. Due to the rapid advance in VLSI technology, parallel processing, and computer aided design (CAD), application-specific VLSI systems are becoming more and more powerful and feasible. In particular, VLSI array processors offer high speed and efficiency through their massive parallelism and pipelining, regularity, modularity, and local communication. A unified VLSI array architecture can be used for implementing neural networks and Hidden Markov Models. He also proposes a pipeline interleaving approach to design VLSI array architectures for real-time image and video signal processing.

• Research Organization: Princeton Univ., NJ (United States)
• OSTI ID: 5521602
• Resource Relation: Other Information: Thesis (Ph.D.)
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
NEURAL NETWORKS
ALGORITHMS
COMPUTER ARCHITECTURE
ARRAY PROCESSORS
ARTIFICIAL INTELLIGENCE
INTEGRATED CIRCUITS
LEARNING
PARALLEL PROCESSING
PATTERN RECOGNITION
RESEARCH PROGRAMS
SIGNALS
ELECTRONIC CIRCUITS
MATHEMATICAL LOGIC
MICROELECTRONIC CIRCUITS
PROGRAMMING
990200* - Mathematics & Computers