4 research outputs found
Parallel simulation of character recognition problems using NEUCOMP2
NEUCOMP2 is a parallel Neural Network Compiler for a shared-memory parallel machine. It compiles a program written as a list of mathematical specifications of Neural Network (NN) models and then translates it into a chosen target program which contains parallel codes. Performance results for character recognition problems on popular NN models are presented. The models are the backpropagation, Kohonen, Counterpropagation and ART1 network models. NEUCOMP2 was developed and run on the SEQUENT Balance 8000 computer system at PARC
The design of a neural network compiler
Computer simulation is a flexible and economical way for
rapid prototyping and concept evaluation with Neural
Network (NN) models. Increasing research on NNs has led
to the development of several simulation programs. Not
all simulations have the same scope. Some simulations
allow only a fixed network model and some are more
general. Designing a simulation program for general
purpose NN models has become a current trend nowadays
because of its flexibility and efficiency. A proper
programming language specifically for NN models is
preferred since the existing high-level languages such as
C are for NN designers from a strong computer background.
The program translations for NN languages come from
combinations which are either interpreter and/or
compiler. There are also various styles of programming
languages such as a procedural, functional, descriptive
and object-oriented.
The main focus of this thesis is to study the
feasibility of using a compiler method for the
development of a general-purpose simulator - NEUCOMP that
compiles the program written as a list of mathematical
specifications of the particular NN model and translates
it into a chosen target program. The language supported
by NEUCOMP is based on a procedural style. Information
regarding the list of mathematical statements required by
the NN models are written in the program. The
mathematical statements used are represented by scalar,
vector and matrix assignments. NEUCOMP translates these
expressions into actual program loops.
NEUCOMP enables compilation of a simulation program
written in the NEUCOMP language for any NN model,
contains graphical facilities such as portraying the NN
architecture and displaying a graph of the result during
training and finally to have a program that can run on a
parallel shared memory multi-processor system
Worker Productivity: A Fuzzy Supervised Neural Training Algorithm Approach
Productivity refers to the physical relation between the quality produced (output) and the quantity of resource used in the course of production (input). Productivity is a relative term indicating the ratio between total output and the total inputs used therein on the other hand production is an absolute concept, which refers to the volume of output. Fuzzy Supervised Neural Network Training Algorithm has been designed and implemented with Matrix Laboratory (MATLAB) and Hypertext Preprocessor as the simulation language. This paper demonstrates the practical application of soft computing algorithm techniques in various well-meaning organizations
Worker Productivity: A Fuzzy Supervised Neural Training Algorithm Approach
Productivity refers to the physical relation between the quality produced (output) and the quantity of resource used in the course of production (input). Productivity is a relative term indicating the ratio between total output and the total inputs used therein on the other hand production is an absolute concept, which refers to the volume of output. Fuzzy Supervised Neural Network Training Algorithm has been designed and implemented with Matrix Laboratory (MATLAB) and Hypertext Preprocessor as the simulation language. This paper demonstrates the practical application of soft computing algorithm techniques in various well-meaning organizations