الفهرس 
MANUFACTURING SYSTEMSOnly 14 pages are availabe for public view
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Abstract
Work on artificial neural networks has been motivated right from
its inception by the recognition that the human brain computes in an
entirely different way from the conventional digital computer. The
brain is a highly complex, nonlinear, and parallel computer
(information - processing system). A neural network is a massively
parallel distributed processor made up of simple processing units,
which has a natural propensity for storing experiential knowledge
and making it available for use. It resembles the brain in two
respects: 1. Knowledge is acquired by the network from its
environment through a learning process. 2. Interneuron connection
strengths, known as synaptic weights, are used to store the acquired
knowledge.
The manufacturing systems developed considerably during the
last decade. They became extremely complex and difficult to
control. In today’s highly competitive market, quality and
productivity have become the pnmary goals for which
manufacturers strive vigorously. To answer these requirements, it is
necessary to have powerful tools for modeling, analysis, and
control.
The intent of thesis is to develop a neural network based - control
schema to model and control, complex and continuous
manufacturing systems using artificial neural networks.
Modeling any complex system of manufacturing systems by
neural networks starts by modeling separately the basic elements of
these systems by neural models. These different models are then connected together to form the overall studied system. This
.
approach is illustrated using an example of open / closed loop
modeling manufacturing systems, model that is consisting of four
stations; each one is made up of a stock with limited capacity and a
machine.
The thesis develops an integration activation function in neural
networks to model continuous manufacturing systems, develop a
min (x, y) function and a learning algorithm to control modeled
manufacturing systems. The learning algorithm depends on backpropagation
learning algorithm with some modifications to be
effective only on some weights.
The simulation results for the modeled manufacturing systems
show also the control applied to the model. Neural network
controller has the better minimum energy and smoothing with
changing the speed of machines than the petri net controller.
The thesis presents two other applications. The first application
is to develop an intelligent controller to control ship direction. This
requires a procedure to acquire the control rules for a moving ship
to avoid collision with another moving object and then steer back to
reach a certain destination. This objective can be achieved by
applying the proposed modeling and control algorithms. The second
application is to develop an intelligent controller for missile
tracking. The proposed neural network controller has the best
maximum proximity, and smoothing in direction compared with
expert and fuzzy controllers, but low sum of energy.
Simulation results for the motion of the controlled ship using the
neural network controller showed improved performance of the
motion control and the potential of the proposed control schema of
the ship.