Neural Networks and Conventional Computers - Essay Example

NEURAL NETWORK Introduction Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across anextraordinary range of problem domains, in areas as diverse as medicine, engineering, geology, finance and physics. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. The term neural network has been used for a century or more to describe the networks of biological neurons that constitute the nervous systems of animals, whether invertebrates or vertebrates. Since the 1940s, and especially since the early 1980s, the term has also been used for a technology of parallel computation in which the computing elements are 'artificial neurons' loosely modeled on simple properties of biological neurons, usually with some adaptive capability to change the strengths of connections between the neurons. In general a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. These connections are called synapses, are usually formed from axons to dendrites, though dendrodendritic microcircuits and other connections are possible. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. As such, neural networks are extremely complex (Arbib 2002). Now a day the term neural network often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages: Biological neural networks which are made up of real biological neurons. These Biological neural networks are connected or functionally related in the peripheral nervous system or the central nervous system. They are often identified as groups of neurons that perform a specific physiological function in laboratory analysis. Artificial neural networks are made up of interconnecting artificial neurons that mimic the properties of biological neurons. Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an 'expert' in the category of information it has been given to analyze. After analyzing, this expert answers the 'what if' questions. Other advantages of Neural Network include: Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience. Self-Organization: An Artificial Neural Network can create its own organization or representation of the information it receives during learning time. Real Time Operation: Artificial Neural Network computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability. Fault Tolerance via Redundant Information Coding: Partial destruction of a leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage. Biological Neural Networks Most living creatures, which have the ability to adapt to a changing environment, need a controlling unit which is able to learn. Higher developed animals and humans use very complex networks of highly specialized neurons to perform this task. The control unit or brain can be divided in different anatomic and functional sub-units, each having certain tasks like vision, hearing, motor and sensor control. The brain is connected by nerves to the sensors and actors in the rest of the body. The brain consists of a very large number of neurons, about 1011 in average. These can be seen as the basic building bricks for the central nervous system (CNS). The neurons are interconnected at points called synapses. The complexity of the brain is due to the massive number of highly interconnected simple units working in parallel, with an individual neuron receiving input from up to 10000 others. The neuron contains all structures of an animal cell. The complexity of the structure and of the processes in a simple cell is enormous. Even the most sophisticated neuron models in artificial neural networks seem comparatively toy-like. Structurally the neuron can be divided in three major parts: the cell body (soma), the dendrites, and the axon, as shown in figure 1. Figure 1: Simplified Biological Neurons. The cell body contains the organelles of the neuron and also the dendrites' are originating there. These are thin and widely branching fibers, reaching out in different directions to make connections to a larger number of cells within the cluster. Input connection is made from the axons of other cells to the dendrites or directly to the body of the cell. These are known as axondendrititic and axonsomatic synapses. There is only one axon per neuron. It is a single and long fiber, which transports the output signal of the cell as electrical impulses (action potential) along its length. The end of the axon may divide in many branches, which are then connected to other cells. The branches have the function to fan out the signal to many other inputs. There are many different types of neuron cells found in the nervous system. The differences are due to their location and function. The neurons perform basically the following function: All the inputs to the cell, which may vary by the strength of the connection or the frequency of the incoming signal, are summed up. The input sum is processed by a threshold function and produces an output signal. The processing time of about 1ms per cycle and transmission speed of the neurons of about 0.6 to 120 ms is comparatively slow to a modern computer (Zell 94), (Barr 88) The brain works in both a parallel and serial way. The parallel and serial nature of the brain is readily apparent from the physical anatomy of the nervous system. The serial and parallel processing involved can be easily seen from the time needed to perform tasks. For example, a human can recognize the picture of another person in about 100 ms. Given the processing time of 1 ms for an individual neuron this implies that a certain number of neurons, but less than 100, are involved in serial; whereas the complexity of the task is evidence for a parallel processing, because a difficult recognition task can not be performed by such a small number of neurons (Zell 94). Biological neural systems usually have a very high fault tolerance. Experiments with people with brain injuries have shown that damage of neurons up to a certain level does not necessarily influence the performance of the system, though tasks such as writing or speaking may have to be learned again. This can be regarded as re-training the network. Artificial Neural Network An Artificial Neural Network is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in union to solve specific problems. An Artificial Neural Network, like people, learns by example. It is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of an Artificial Neural Network as well. An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system. Although computing these days is truly advanced, there are certain tasks that a program made for a common microprocessor is unable to perform; even so a software implementation of a neural network can be made with their advantages and disadvantages. Advantages of Artificial Neural Network: A neural network can perform tasks that a linear program can not. When an element of the neural network fails, it can continue without any problem by their parallel nature. A neural network learns and does not need to be reprogrammed. It can be implemented in any application. It can be implemented without any problem. Disadvantages of Artificial Neural Network: The neural network needs training to operate. The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated. Requires high processing time for large neural networks. Another aspect of the artificial neural networks is that there are different architectures, which consequently requires different types of algorithms, but despite to be an apparently complex system, a neural network is relatively simple. Neural networks and conventional computers Neural networks take a different approach to problem solving than that of conventional computers. Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order to solve a problem. Unless the specific steps that the computer needs to follow are known, the computer cannot solve the problem. That restricts the problem solving capability of conventional computers to problems that we already understand and know how to solve. But computers would be so much more useful if they could do things that we don't exactly know how to do. Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem. Neural networks learn by example. They cannot be programmed to perform a specific task. The examples must be selected carefully otherwise useful time is wasted or even worse the network might be functioning incorrectly. The disadvantage is that because the network finds out how to solve the problem by itself, its operation can be unpredictable. On the other hand, conventional computers use a cognitive approach to problem solving; the way the problem is to solved must be known and stated in small unambiguous instructions. These instructions are then converted to a high level language program and then into machine code that the computer can understand. These machines are totally predictable; if anything goes wrong is due to a software or hardware fault. Neural networks and conventional algorithmic computers are not in competition but complement each other. There are tasks more suited to an algorithmic approach like arithmetic operations and tasks that are more suited to neural networks. Even more, a large number of tasks, require systems that use a combination of the two approaches (normally a conventional computer is used to supervise the neural network) in order to perform at maximum efficiency. Applications of Neural Network Neural Networks in Marketing and Practice Neural networks have broad applicability to real world business problems. In fact, they have already been successfully applied in many industries. Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including sales forecasting, industrial process control, customer research, data validation, risk management, target marketing etc. Almost any neural network application would fit into one business area or financial analysis. There is some potential for using neural networks for business purposes, including resource allocation and scheduling. There is also a strong potential for using neural networks for database mining, that is, searching for patterns implicit within the explicitly stored information in databases. Neural Networks in Medicine Artificial Neural Networks are currently a 'hot' research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modeling parts of the human body and recognizing diseases from various scans (e.g. cardiograms, CAT scans, ultrasonic scans, etc.). Neural networks are ideal in recognizing diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognize the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. Modeling and Diagnosing the Cardiovascular System: Neural Networks are used experimentally to model the human cardiovascular system. Diagnosis can be achieved by building a model of the cardiovascular system of an individual and comparing it with the real time physiological measurements taken from the patient. If this routine is carried out regularly, potential harmful medical conditions can be detected at an early stage and thus make the process of combating the disease much easier. Electronic noses: Electronic noses have several potential applications in telemedicine which is the practice of medicine over long distances via a communication link. The electronic nose would identify odors in the remote surgical environment which would then be electronically transmitted to another site where a generation system would recreate them. Because the sense of smell can be an important sense to the surgeon, telesmell would enhance telepresent surgery. Instant Physician: This application of neural network was developed in the mid-1980s which is called as 'instant physician'. An auto-associative memory neural network stores a large number of medical records, each of which includes information on symptoms, diagnosis, and treatment for a particular case. After training, the net can be presented with input consisting of a set of symptoms; it will then find the full stored pattern that represents the 'best' diagnosis and treatment (Carling 1992). Conclusion The concept of neural networks started in the late-1800s as an effort to describe how the human mind performs. The computing world has a lot to gain from neural networks. Neural Networks approaches the problem by trying to mimic the structure and function of our nervous system. Neural networks process information in a similar way the human brain does. They are regularly used to model different parts of living organisms and to investigate the internal mechanisms of the brain. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem and learn by examples. Their ability to learn by example makes them very flexible and powerful. Furthermore, there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture. The Neural Network can be used when the influencing variables on a certain event are not exactly known as it is the case in financial or weather forecasts. This article aims to give a short overview on the functions of Neural Network and their applications in the field of medicine, engineering, marketing, neurology, psychology, finance, geology and physics. Present work introduces the background of Neural Network and outlines the basic concepts crucially important for understanding more sophisticated Artificial Neural Network. Finally, I would like to state that even though neural networks have a huge potential, we will only get the best of them when they are integrated with different computing systems and subjects. Reference 1. Arbib, Michael A. (Ed). 'The Handbook of Brain Theory and Neural Networks', IInd Edition, The MIT Press Cambridge, ISBN-13: 978-0-262-01197-6, 2002, p. 666 2. Sheng-Wei Zhang and T.J. Stonham. Universal Architectures for Logical Neural Networks. In: Second International Conference on Artificial Neural Networks, Conference Publication No. 349 IEE,18-20 November 1991, p 24. 3. Murray L. Barr & John A. Kiernan. 'The Human Nervous System. An Anatomical Viewpoint'. Fifth Edition. Harper Internationa,l 1988, p 35. 4. Carling, A., Introducing Neural Networks. Wilmslow, UK: Sigma Press, 1992 Read More