DIRECT TORQUE CONTROL OF INDUCTION MOTOR USING ARTIFICIAL NEURAL NETWORK

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INTRODUCTION
A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships . In the broader sense, a neural network is a collection of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. It is composed of a large number of highly interconnected processing elements that are analogous to neurons and are tied together with weighted connections that are analogous to synapses.


RESEMBLENCE WITH BRAIN



The brain is principally composed of about 10 billion neurons , each connected to about 10,000 other neurons. Each neuron receives electrochemical inputs from other neurons at the dendrites. If the sum of these electrical inputs is sufficiently powerful to activate the neuron, it transmits an electrochemical signal along the axon, and passes this signal to the other neurons whose dendrites are attached at any of the axon terminals. These attached neurons may then fire.


STRUCTURE OF NEURAL NETWORK

According to Frank Rosenblatt’s theory in 1958 ,the basic element of a neural network is the perceptron, which in turn has 5 basic elements: an n-vector input, weights, summing function, threshold device, and an output. Outputs are in the form of -1 and/or +1. The threshold has a setting which governs the output based on the summation of input vectors. If the summation falls below the threshold setting, a -1 is the output. If the summation exceeds the threshold setting, +1 is the output. Figure 3.1 depicts the structure of a basic perceptron which is also called artificial neuron.


ARCHITECTURE OF NEURAL NETWORK

.Feed-forward networks:-
Feed-forward ANNs allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed- forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition. This type of organisation is also referred to as bottom-up or top-down.
Feed-back networks:-
Feed-back networks can have signals travelling in both directions by introducing loops in the network. Feedback networks are very powerful and can get extremely complicated. Feedback networks are dynamic; their 'state' is changing continuously until they reach an equilibrium point. They remain at the equilibrium point until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent, although the latter term is often used to denote feedback connections in single-layer organisations.
Network layers:-
The commonest type of artificial neural network consists of three groups, or layers, of units: a layer of “input” units is connected to a layer of “ hidden” units , which is connected to a layer of “output” units.
1.The activity of the input units represents the raw information that is fed into the network.
2. The activity of each hidden unit is determined by the activities of the input units and the weights on the connections between the input and the hidden units.
3. The behavior of the output units depends on the activity of the hidden units and the weights between the hidden and output units.