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Chapter: Artificial Intelligence

Neural Network

A neural network consists of inter connected processing elements called neurons that work together to produce an output function.



A neural network consists of inter connected processing elements called neurons that work together to produce an output function. The output of a neural network relies on the cooperation of the individual neurons within the network to operate. Well designed neural networks are trainable systems that can often “learn” to solve complex problems from a set of exe mplars and generalize the “acquired knowledge” to solve unforeseen problems, i.e. they are self-adaptive systems. A neural network is used to refer to a network of biological neurons. A neural network consists of a set of highly interconnected entities called nodes or units. Each unit accepts a weighted set of inputs and responds with an output.


A neural network is first and foremost a graph, with patterns represented in terms of numerical values attached to the nodes of the graph and transformations between patterns achieved via simple message-passing algorithms. The graph contains a number of units and weighted unidirectional connections between them. The output of one unit typically becomes an input for another. There may also be units with external inputs and outputs. The nodes in the graph are generally distinguished as being input nodes or output nodes and the graph as a whole can be viewed as a representation of a multivariate functions linking inputs to outputs. Numerical values (weights) are attached to the links of the graphs, parameterizing the input/ output function and allowing it to be adjusted via a learning algorithm. A broader view of a neural network architecture involves treating the network as a statistical processor characterized by making particular probabilistic assumptions about data. Figure illustrates one example of a possible neural network structure.



Patterns appearing on the input nodes or the output nodes of a network are viewed as samples from probability densities and a network is viewed as a probabilistic model that assigns probabilities to patterns. Biologically, we can also define a neuron. The human body is made up of a vast array of living cells. Certain cells are interconnected in a way that allows them to communicate pain or to actuate fibres or tissues. Some cells control the opening and closing of minuscule valves in the veins and arteries. These specialized communication cells are called neurons. Neurons are equipped with long tentacle like structures that stretch out from the cell body, permitting them to communicate with other neurons. The tentacles that take in signals from other cells and the environment itself are called dendrites, while the tentacles that carry signals from the neuron to other cells are called axons.


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