Neural Networks

A neural network also known as an artificial neural network provides a

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unique computing architecture whose potential has only begun to be tapped. They

are used to address problems that are intractable or cumbersome with traditional

methods. These new computing architectures are radically different from the

computers that are widely used today. ANN's are massively parallel systems that

rely on dense arrangements of interconnections and surprisingly simple

processors (Cr95, Ga93).

Artificial neural networks take their name from the networks of nerve

cells in the brain. Although a great deal of biological detail is eliminated in

these computing models, the ANN's retain enough of the structure observed in the

brain to provide insight into how biological neural processing may work (He90).

Neural networks provide an effective approach for a broad spectrum of

applications. Neural networks excel at problems involving patterns, which

include pattern mapping, pattern completion, and pattern classification (He95).

Neural networks may be applied to translate images into keywords or even

translate financial data into financial predictions (Wo96).

Neural networks utilize a parallel processing structure that has large

numbers of processors and many interconnections between them. These processors

are much simpler than typical central processing units (He90). In a neural

network, each processor is linked to many of its neighbors so that there are

many more interconnections than processors. The power of the neural network

lies in the tremendous number of interconnections (Za93).

ANN's are generating much interest among engineers and scientists.

Artificial neural network models contribute to our understanding of biological

models. They also provide a novel type of parallel processing that has powerful

capabilities and potential for creative hardware implementations, meets the

demand for fast computing hardware, and provides the potential for solving

application problems (Wo96).

Neural networks excite our imagination and relentless desire to

understand the self, and in addition, equip us with an assemblage of unique

technological tools. But what has triggered the most interest in neural

networks is that models similar to biological nervous systems can actually be

made to do useful computations, and furthermore, the capabilities of the

resulting systems provide an effective approach to previously unsolved problems


Neural network architectures are strikingly different from traditional

single-processor computers. Traditional Von Neumann machines have a single CPU

that performs all of its computations in sequence (He90). A typical CPU is

capable of a hundred or more basic commands, including additions, subtractions,

loads, and shifts. The commands are executed one at a time, at successive steps

of a time clock. In contrast, a neural network processing unit may do only one,

or, at most, a few calculations. A summation function is performed on its

inputs and incremental changes are made to parameters associated with

interconnections. This simple structure nevertheless provides a neural network

with the capabilities to classify and recognize patterns, to perform pattern

mapping, and to be useful as a computing tool (Vo94).

The processing power of a neural network is measured mainly be the

number of interconnection updates per second. In contrast, Von Neumann machines

are benchmarked by the number of instructions that are performed per second, in

sequence, by a single processor (He90). Neural networks, during their learning

phase, adjust parameters associated with the interconnections between neurons.

Thus, the rate of learning is dependent on the rate of interconnection updates


Neural network architectures depart from typical parallel processing

architectures in some basic respects. First, the processors in a neural network

are massively interconnected. As a result, there are more interconnections than

there are processing units (Vo94). In fact, the number of interconnections

usually far exceeds the number of processing units. State-of-the-art parallel

processing architectures typically have a smaller ratio of interconnections to

processing units (Za93). In addition, parallel processing architectures tend to

incorporate processing units that are comparable in complexity to those of Von

Neumann machines (He90). Neural network architectures depart from this

organization scheme by containing simpler processing units, which are designed

for summation of many inputs and adjustment of interconnection parameters.

The two primary attractions that come from the computational viewpoint

of neural networks are learning and knowledge representation. A lot of

researchers feel that machine learning techniques will give the best hope for

eventually being able to perform difficult artificial intelligence tasks (Ga93).

Most neural networks learn from examples, just like children learn to

recognize dogs from examples of dogs (Wo96). Typically, a neural network is

presented with a training set consisting of a group of examples from which the

network can learn. These examples, known as training patterns, are represented

as vectors, and can be taken from such sources as images, speech signals, sensor

data, and diagnosis information (Cr95, Ga93).

The most common training scenarios utilize supervised learning, during

which the network is presented with an input pattern together with the target

output for that pattern. The target output usually constitutes the correct

answer, or correct classification for the input pattern. In response to these

paired examples, the neural network adjusts the values of its internal weights

(Cr95). If training is successful, the internal parameters are then adjusted to

the point where the network can produce the correct answers in response to each

input pattern (Za93).

Because they learn by example, neural networks have the potential for

building computing systems that do not need to be programmed (Wo96). This

reflects a radically different approach to computing compared to traditional

methods, which involve the development of computer programs. In a computer

program, every step that the computer executes is specified in advance by the

network. In contrast, neural nets begin with sample inputs and outputs, and

learns to provide the correct outputs for each input (Za93).

The neural network approach does not require human identification of

features. It also doesn't require human development of algorithms or programs

that are specific to the classification problem at hand. All of this will

suggest that time and human effort can be saved (Wo96). There are drawbacks to

the neural network approach, however. The time to train the network may not be

known, and the process of designing a network that successfully solves an

applications problem may be involved. The potential of the approach, however,

appears significantly better than past approaches (Ga93).

Neural network architectures encode information in a distributed fashion.

Typically the information that is stored in a neural network is shared by many

of its processing units. This type of coding is in stark contrast to

traditional memory schemes, where particular pieces of information are stored in

particular locations of memory. Traditional speech recognition systems, for

example, contain a lookup table of template speech patterns that are compared

one by one to spoken inputs. Such templates are stored in a specific location

of the computer memory. Neural networks, in contrast, identify spoken syllables

by using a number of processing units simultaneously. The internal

representation is thus distributed across all or part of the network.

Furthermore, more than one syllable or pattern may be stored at the same time by

the same network (Ze93).

Neural networks have far-reaching potential as building blocks in

tomorrow's computational world. Already, useful applications have been designed,

built, and commercialized, and much research continues in hopes of extending

this success (He95).

Neural network applications emphasize areas where they appear to offer a

more appropriate approach than traditional computing has. Neural networks offer

possibilities for solving problems that require pattern recognition, pattern

mapping, dealing with noisy data, pattern completion, associative lookups, and

systems that learn or adapt during use (Fr93, Za93). Examples of specific areas

where these types of problems appear include speech synthesis and recognition,

image processing and analysis, sonar and seismic signal classification, and

adaptive control. In addition, neural networks can perform some knowledge

processing tasks and can be used to implement associative memory (Kh90). Some

optimization tasks can be addressed with neural networks. The range of

potential applications is impressive.

The first highly developed application was handwritten character

identification. A neural network is trained on a set of handwritten characters,

such as printed letters of the alphabet. The network training set then consists

of the handwritten characters as inputs together with the correct identification

for each character. At the completion of training, the network identifies

handwritten characters in spite of the variations (Za93).

Another impressive application study involved NETtalk, a neural network

that learns to produce phonetic strings, which in turn specify pronunciation for

written text. The input to the network in this case was English text in the

form of successive letters that appear in sentences. The output of the network

was phonetic notation for the proper sound to produce given the text input. The

output was linked to a speech generator so that an observer could hear the

network learn to speak. This network, trained by Sejnowski and Rosenberg,

learned to pronounce English text with a high level of accuracy (Za93).

Neural network studies have also been done for adaptive control

applications. A classic implementation of a neural network control system was

the broom-balancing experiment, originally done by Widrow and Smith in 1963.

The network learned to move a cart back and forth in such a way that a broom

balanced upside-down on its handle tip and the cart remained on end (Da90).

More recently, application studies were done for teaching a robotic arm how to

get to its target position, and for steadying a robotic arm. Research was also

done on teaching a neural network to control an autonomous vehicle using

simulated, simplified vehicle control situations (Wo96).

Neural networks are expected to complement rather than replace other

technologies. Tasks that are done well by traditional computer methods need not

be addressed with neural networks, but technologies that complement neural

networks are far-reaching (He90). For example, expert systems and rule-based

knowledge-processing techniques are adequate for some applications, although

neural networks have the ability to learn rules more flexibly. More

sophisticated systems may be built in some cases from a combination of expert

systems and neural networks (Wo96). Sensors for visual or acoustic data may be

combined in a system that includes a neural network for analysis and pattern

recognition. Robotics and control systems may use neural network components in

the future. Simulation techniques, such as simulation languages, may be

extended to include structures that allow us to simulate neural networks.

Neural networks may also play a new role in the optimization of engineering

designs and industrial resour ces (Za93).

Many design choices are involved in developing a neural network application.

The first option is in choosing the general area of application. Usually this

is an existing problem that appears amenable to solutions with a neural network.

Next the problem must be defined specifically so that a selection of inputs and

outputs to the network may be made. Choices for inputs and outputs involve

identifying the types of patterns to go into and out of the network. In

addition, the researcher must design how those patterns are to represent the

needed information. Next, internal design choices must be made. This would

include the topology and size of the network (Kh90). The number of processing

units are specified, along with the specific interconnections that the network

is to have. Processing units are usually organized into distinct layers, which

are either fully or partially interconnected (Vo95).

There are additional choices for the dynamic activity of the processing

units. A variety of neural net paradigms are available. Each paradigm dictates

how the readjustment of parameters takes place. This readjustment results in

learning by the network. Next there are internal parameters that must be tuned

to optimize the ANN design (Kh90). One such parameter is the learning rate from

the back-error propagation paradigm. The value of this parameter influences the

rate of learning by the network, and may possibly influence how successfully the

network learns (Cr95). There are experiments that indicate that learning occurs

more successfully if this parameter is decreased during a learning session.

Some paradigms utilize more than one parameter that must be tuned. Typically,

network parameters are tuned with the help of experimental results and

experience on the specific applications problem under study (Kh90).

Finally, the selection of training data presented to the neural network

influences whether or not the network learns a particular task. Like a child,

how well a network will learn depends on the examples presented. A good set of

examples, which illustrate the tasks to be learned well, is necessary for the

desired learning to take place. The set of training examples must also reflect

the variability in the patterns that the network will encounter after training


Although a variety of neural network paradigms have already been

established, there are many variations currently being researched. Typically

these variations add more complexity to gain more capabilities (Kh90). Examples

of additional structures under investigation include the incorporation of delay

components, the use of sparse interconnections, and the inclusion of interaction

between different interconnections. More than one neural net may be combined,

with outputs of some networks becoming the inputs of others. Such combined

systems sometimes provide improved performance and faster training times (Da90).

Implementations of neural networks come in many forms. The most widely

used implementations of neural networks today are software simulators. These

are computer programs that simulate the operation of the neural network. The

speed of the simulation depends on the speed of the hardware upon which the

simulation is executed. A variety of accelerator boards are available for

individual computers to speed the computations (Wo96).

Simulation is key to the development and deployment of neural network

technology. With a simulator, one can establish most of the design choices in a

neural network system. The choice of inputs and outputs can be tested as well

as the capabilities of the particular paradigm used (Wo96).

Implementations of neural networks are not limited to computer simulation,

however. An implementation could be an individual calculating the changing

parameters of the network using pencil and paper. Another implementation would

be a collection of people, each one acting as a processing unit, using a hand-

held calculator (He90). Although these implementations are not fast enough to

be effective for applications, they are nevertheless methods for emulating a

parallel computing structure based on neural network architectures (Za93).

One challenge to neural network applications is that they require more

computational power than readily available computers have, and the tradeoffs in

sizing up such a network are sometimes not apparent from a small-scale

simulation. The performance of a neural network must be tested using a network

the same size as that to be used in the application (Za93).

The response of an ANN may be accelerated through the use of specialized

hardware. Such hardware may be designed using analog computing technology or a

combination of analog and digital. Development of such specialized hardware is

underway, but there are many problems yet to be solved. Such technological

advances as custom logic chips and logic-enhanced memory chips are being

considered for neural network implementations (Wo96).

No discussion of implementation would be complete without mention of the

original neural networks, which is the biological nervous systems. These

systems provided the first implementation of neural network architectures. Both

systems are based on parallel computing units that are heavily interconnected,

and both systems include feature detectors, redundancy, massive parallelism, and

modulation of connections (Vo94, Gr93).

However the differences between biological systems and artificial neural

networks are substantial. Artificial neural networks usually have regular

interconnection topologies, based on a fully connected, layered organization.

While biological interconnections do not precisely fit the fully connected,

layered organization model, they nevertheless have a defined structure at the

systems level, including specific areas that aggregate synapses and fibers, and

a variety of other interconnections (Lo94, Gr93). Although many connections in

the brain may seem random or statistical, it is likely that considerable

precision exists at the cellular and ensemble levels as well as the system level.

Another difference between artificial and biological systems arises from the

fact that the brain organizes itself dynamically during a developmental period,

and can permanently fix its wiring based on experiences during certain critical

periods of development. This influence on connection topology does not occur in

c urrent ANN's (Lo94, Da90).

The future of neurocomputing can benefit greatly from biological studies.

Structures found in biological systems can inspire new design architectures for

ANN models (He90). Similarly, biology and cognitive science can benefit from

the development of neurocomputing models. Artificial neural networks do, for

example, illustrate ways of modeling characteristics that appear in the human

brain (Le91). Conclusions, however, must be carefully drawn to avoid confusion

between the two types of systems.


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21, 1995), pp 1075.

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1993), pp 19.

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Massachusetts, 1993.

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Massachusetts, 1993.

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