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What is a network of neurons? Especially when we’re talking about a neural network, we need more fair to say “artificial neural networks (ANN), because that is what we hear most often. Biological neural networks are much more complex than mathematical models that we used for RNA. But it is customary to be lazy and drop the “A” or “artificial”. An artificial neural network (ANN) is a paradigm of information processing, which is inspired by the biological nervous system, such as the brain to process information. The key element of this paradigm is the new structure of the data processing system. It’s a large number of highly interconnected processing elements (neurons) to cooperate together to solve specific problems. RNA, as people learn by example. An ANN is a specific application, such as pattern recognition or data classification is configured through a learning process. Learning in biological systems requires an adjustment of the synaptic connections between neurons are present. This also applies ANN. History of neural networks simulations of neural networks seem to be a new development. However, this area was established before the advent of computers and has survived at least one major setback and several times. Much progress has been Importand reinforced by the use of inexpensive computer emulations. After an initial period of enthusiasm, the field survived a period of frustration and disrepute. Meanwhile in the minimal funding and professional support is important progress has been made by relatively few reserch. These pioneers were able, persuasive technology, the development has exceeded the limits defined by Minsky and Papert. Minsky and Papert published a book (1969), which describes a general feeling of frustration (against neural networks) among researchers, and has been accepted by most without further analysis. Currently, the field of neural networks has renewed interest and a corresponding increase in funding. The history of neural networks, which are described above are divided into several phases: First Test: There were initial simulations using formal logic. McCulloch and Pitts (1943) developed models of neural networks based on their understanding of neurology. These models based on different assumptions about how neurons worked. Their neural networks were also based simple binary devices with fixed thresholds. The results of the model are simple logic functions such as “A or B” and “A and B”. Another attempt has been using computer simulations. Two groups (Farley and Clark, 1954; Rochester, Holland, Haiba and Duda, 1956). The first group (IBM Reserch) kept closed contact with neuroscientists at McGill University. So, if their models can not work, the neurologist consulted. This interaction has been a trend that continues in multidiscilinary present. psychologists and promising emerging technologies: not only helped neroscience influential in the development of networks of neurons, but also engineers and progress of neural network simulations. Rosenblatt (1958) generated considerable interest and activity in the field when he designed and developed the Perceptron. The Perceptron had three layers with the middle layer and the layer of the club known. This system could learn to tie or connect a given input to a production unit random. Another system was the Adaline (adaptive linear element), which was developed in 1960 by Widrow and Hoff (Stanford University). The Adaline was an electronic analog of simple components. The learning method was different than the Perceptron, is the least-mean square (LMS) learning rule employs. Period of frustration and disrepute: In 1969, Minsky and Papert wrote a book in which they are layered in the limit of single layer perceptron generalized systems. In the book, she said: “… our intuitive verdict that the extension (for multi-layer) is “sterile. The main result of his book was the funding of research with simulations of neural network to be removed. The findings supported the disenhantment reserch in the field. was activated as a result considerable damage in this area. Innovation: Although public interest and available resources were minimal, many researchers continue to work on developing neuromorphically computaional methods based on problems such as pattern recognition. During this period several paradigms were generated to improve the modern work. Grossberg’s (Steve Grossberg and Gail Carpenter in 1988) influence founded a school of thought, the resonance of the algorithms studied. They developed the art (Adaptive Resonance Theory) networks biological plausible models. Anderson and Kohonen developed associative techniques independent of each other. Knock (Knock A. Henry) in 1972, has developed a database of learning in artificial neurons based on a biological principle of learning called neural hetero stasis. Formulated (Paul Formulated 1974) developed and used the backpropagation learning method, however, several years before this concept was adopted popularized. Backpropagation networks are probably the best known and most widely used neural networks used today. In essence, the backpropagation network. is a multilayer perceptron, a different function in artificial neural thershold, and a rule more robust and capable of learning. Amari (A. Shun-Ichi 1967) was involved in theoretical developments: he published a paper, a mathematical theory of a training base (method of correction of errors), which is fixed to the classification adaptive Patern. Although Fukushima (F. Kunihiko) developed an unwise trained multilayer neural networks for interpretation of handwritten characters. The original network was published in 1975 and was admitted cognitronic. Re-Emergence: Progress during the late 1970s and early 1980s, it was important to the re-emergence of interest in the field of neural networks. Several factors have influenced this movement. For example, when a large books and conferences are a forum for people in different fields with the specialized technical language, and the response to conferences and publications has been very positive. The supported media and increased activity of tutorials helped disseminate the technology. Curricula and courses inroduced appeared in most universities (USA and Europe). The focus is now on funding levels across Europe, Japan and the United States and how this money is concentrated is available to create several new commercial applications in industry and finacial institutions. Today: Considerable progress has been in the field of neural networks have been sufficient to finance great attention and acquire new researches. Promoting the applications current trade is possible and research is making progress on several fronts. based chips and neurons are developing applications for complex problems. Of course, today is a transitional period for the neural network technology. Why use neural networks? Neural networks come with their remarkable ability to sense from complex or imprecise data, can be used to extract patterns and detect trends that are too complex, are being noticed by humans or other computer techniques . A neural network can be considered an “expert” in the category of information has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer “what-if” problems. Other advantages are: Adaptive Learning to learn: the ability to do tasks such as data due to training or initial experience. Self-organization: an RNA can have their own organization or representation of information obtained during the learning period. Real Time Operation: ANN computations may be performed in parallel, and special hardware devices are designed and manufactured to take advantage of this opportunity. The fault tolerance by redundancy coding of information: the partial destruction of a network led to the corresponding decrease in performance. However, certain network features are maintained even with large net losses. Neural networks on classical computers Neural networks have a different approach to solve problems that classical computers. Conventional computers use an algorithmic approach ie the computer follows a set of instructions for solving a problem. Unless the specific steps that the computer is known to follow the computer can not solve the problem should be. This limits the ability to solve problems of classical computers to the problems we already know and how to solve. But computers are much more useful if the things we do not know exactly how you could do. neural information process networks in a similar way the human brain function. The network consists of a large number of highly interconnected processing elements (neurons) working together in parallel to solve a specific problem. Neural networks learn by example. You can not programmed to perform a specific task. The examples are carefully otherwise useful time to be selected wasted or even worse the network might not work properly. The disadvantage is that because the network finds out how to solve the problem itself, its operation can be unpredictable. On the other hand, conventional computers, a cognitive approach to solving problems is how to solve the problem must be known and shown in small unambiguous instructions. These instructions are then converted into a program of high-level language and machine code that the computer can understand. These machines are totally predictable, if something goes wrong is due to a software or hardware failure. Neural networks and conventional algorithms computers are not in competition with each other, but complementary. There are tasks are more suited to an algorithmic approach like arithmetic operations and tasks that are more appropriate neural networks. Even more, many tasks require systems that use a combination of the two approaches (normally a conventional computer uses to monitor the neural network) to play with maximum efficiency. Neural networks in practice From this description of neural networks and how they work, what practical applications are appropriate? Neural networks have broad applicability to real business problems. In fact, they have already successfully used in many industries. As neural networks to identify the best structure or trends in the data, they are well suited for the prediction and forecasting of needs, including: Sales Forecasting Industrial process control Customer Research Data Validation Risk management Targeted marketing But to give you more concrete examples; ANN are also used in specific paradigms: recognition of speakers in communication, diagnosis of hepatitis use of telecommunications from faulty software; interpretation multimeaning Chinese words, mine detection underwater, texture analysis, three-dimensional object recognition, recognition of handwritten words, and face recognition. The Human and Artificial Neural Networks – Analysis of similarities How the human brain learns? Much is still unknown how the brain itself trains to process information, swarming. In the human brain, a typical neuron collects signals from others by a variety of fine structures called dendrites. The neuron sends out spikes of electrical activity through a long, thin as a carrier of an axon, which splits known in thousands of stores. At the end of each branch, a structure called a synapse converts the activity of the axon electrical effects that stimulate or inhibit the activity of the axon into electrical effects that stimulate or inhibit the activity in connected neurons. When entering an excitatory neuron is sufficiently large compared with its inhibitory input received, it sends a little electrical activity down its axon. Learning takes place by the effectiveness of synapses, changes that the influence of one neuron to another. Components of a neuron The Synapse Neurons of the man with artificial neural We conduct these neural networks by first trying to deduce the essential properties of nerve cells and their connections. We then typically program a computer to simulate these characteristics. But because our knowledge of neurons is incomplete and our computing power is limited, our models are necessarily gross idealizations of real networks of neurons. The neuron model Architecture of neural networks feed-forward networks Feed-forward ANNs allow signals to travel in one direction, from input to output. There are no comments (loops), namely the production of any layer does not act on the same plane. RNA feed-forward networks have simpler, as the contributions of personnel results. They are widely used in pattern recognition. This organization is known as a “bottom-up or top-down. Network Assessment evaluation networks (Figure 1) signals can travel in both directions by introducing loops in the network. evaluation networks are very powerful and can be extremely complex. Feedback networks are dynamic, their “state” is constantly changing, until they reach an equilibrium. They remain in the equilibrium point will be found until the input changes and a new balance needs. Architecture of evaluation are also referred to as interactive or recurrent, although the term is often used to denote feedback connections in organizations with a single layer. Application of neural networks Neural networks in practice From this description of neural networks and how they work, what practical applications are appropriate? Neural networks have broad applicability to real business problems. In fact, they have already successfully used in many industries. As neural networks to identify the best structure or trends in the data, they are well suited for the prediction and forecasting of needs, including: Sales Forecasting Industrial process control Customer Research Data Validation Risk management Targeted marketing But to give you more concrete examples; ANN are also used in specific paradigms: recognition of speakers in communication, diagnosis of hepatitis use of telecommunications from faulty software; interpretation multimeaning Chinese words, mine detection underwater, texture analysis, three-dimensional object recognition, recognition of handwritten notes, and face recognition. Neural Networks in Medicine Artificial neural networks (ANN) are currently “hot” research in medicine and is believed to have been largely biomedical applications of systems in coming years. When research is mainly on the modeling of human body parts and the recognition scans of various diseases (eg cardiograms, CAT scans, ultrasound, etc.). Neural networks are ideally in the recognition of diseases through exams because there is no need for a special algorithm, we identify the disease. Neural networks learn by example, details that we recognize the disease, are not required. We need a series of examples that are representative of all variants of the disease. All the examples is not as important as quantity. ” Examples should be selected very carefully if the system reliable and efficient. Modeling and diagnosis of cardiovascular system Neural networks are used as experimental model for human cardiovascular disease. The diagnosis can be achieved through the construction of a model derived from the cardiovascular system of an individual and the comparison with real-time measurements of physiological patient. If this routine is carried out regularly, potential harmful medical conditions can be detected at an early stage, making the process of struggle against the disease much easier. A model of the cardiovascular system must mimic the individual relationship between physiological variables (heart rate, systolic and diastolic blood pressure and respiration rate) at different levels of physical activity. If a model, an individual match is found, it is a model of the physical condition of the individual. The simulator will be able to view the properties of each individual, without adapting to the supervision of an expert. This requires a network of neurons. Another reason that the use of warrants ANN technology, the ability to Ann for the fusion of sensors, which provide the combination of values of several different sensors. Sensor fusion allows RNA complex relationships between the values of individual sensors that otherwise would be lost if the values were analyzed individually would learn. In medical diagnostics and modeling, which means that even if each sensor in a set of sensitive only to a certain physiological variables, ANN for the detection of complex disease by merging data from individual biomedical sensors. Electronic noses ANN experimentally implemented using electronic noses. Electronic noses have a number of possible applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose to identify odors in the environment remote surgery. These odors would be identified electronically to another location, where a generation system is being moved again. As the sense of smell may be an important significance for the surgeon, the surgery would improve telesmell TV. For more information on telemedicine and tele-surgery Electronic noses ANN experimentally implemented using electronic noses. Electronic noses have several potential applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose to identify odors in the environment remote surgery. These odors would be identified electronically to another place where a generation system is being moved again. As the sense of smell may be an important significance for the surgeon, would improve surgery telesmell TV. For more information on telemedicine and tele-surgery Neural networks in business Business is a field with several disciplines such as accounting or financial analysis redirected. Almost every application would be good network of neurons in a business or financial analysis. There are several possibilities for the use of neural networks for commercial purposes, including resource allocation and planning. There is also a strong potential for the use of neural networks for data mining, the user is in the patterns stored in the information implicit explicit databases. Most of the work funded in this area is classified as owner. It is therefore not possible to report on the full scope of work in progress. Most of the work is the application of neural networks that the network of Hopfield-tank for the optimization and scheduling. Marketing There is a marketing application that is integrated with a neural network. The marketing company Tactician (AMT Short as a trademark) is a computer system of various intelligent technologies including expert systems is made. A neural network built using the AMT and has been formed to help reverse the spread of Marketing Supervision missions seat sales. The adaptive neural approach has allowed the free expression of the rule. In addition to changing the environment of the application quickly and continuously, as an adaptation option is an ongoing need. The system is used for monitoring and advice you should book a flight. This information has a direct impact on the profitability of a company and can provide a technological advantage for users of the system. Hutchison [& Stephens, 1987] While it is important that neural networks have been applied to this problem is, it is also important to see that this intelligent technology with expert systems and other approaches can be incorporated to make a viable system. Neural networks have been used to study the influence of the interaction of undefined variables. Although these interactions are not defined, they were used by the nervous system to develop useful conclusions. It is remarkable to see also that neural networks can affect the bottom line. Are there limits for networks of neurons? The main concerns are now the problem of scalability, monitoring, verification and system integration of neural networks in the modern environment. Neural Network programs are sometimes unstable when applied to major problems. The defense, nuclear and space industry on the issue of testing and audit. The mathematical theories used to have to guarantee the performance of a neural network application development. The solution for the time to train and test these systems as smart as us people. There are also other practical problems, such as: to simulate the operational problem when trying parallelism of neural networks. Like most neural networks are simulated on sequential machines, which extends to a very rapid increase in processing requirements such as the size of the problem. Solution: Implementation of neural network directly in hardware, but they still need much development. instability to explain that they will not get the results. The networks function as “black boxes”, whose rules of operation are totally unknown Future Then look to the future is like looking into a crystal ball, so it is better to give some predictions. Each prediction is based on a kind of evidence or well-established trend that clearly leads to the extrapolation of us in a new area. Prediction 1: Neural Networks fascinate you the user-specific systems for education, information and entertainment. “Ralite alternative control”, produced by large environments in terms of their attractiveness to potential, education and entertainment. This is not just a research on the trends of extreme, but it is something that is increasingly part of our daily lives, such as by the growing interest in large Center Entertainment seen in each home. These “needs a program” would lead to user feedback to be effective, but simple and “passive” sensors (eg sensors in the fingertips, gloves, bracelets, or feeling, heart rate, blood pressure, skin-ionization, and so on) could be effective feedback in a neural controller. This could be achieved, for example with sensors that detect heart rate, blood pressure, skin ionization and other variables, to learn the system, with one person, the answer might be correlated state. Prediction 2: Neural networks, artificial intelligence with other technologies, methods for direct culture of nerve tissue, integrated, and other exotic technologies like genetic engineering, we will develop forms of living radical and exotic that man, machine, or hybrid. Prediction 3: The neural network allows us to explore new areas of human kingdoms capabillity previously available only with training and discipline. This particular state of consciousness is observable neurophysiological consiously led to facilitate man-machine system interface required. Completion The computer world has much to gain neural networks Fron. Their ability to learn by example makes them very flexible and powerful. In addition, it is not necessary to develop an algorithm to perform a specific task, namely that it is not necessary to understand the inner workings of this task. They are also very good for real-time systems because of their computational speed suitable times, rapid response, which, by their parallel architecture. Neural networks also contribute to other research areas such as neurology and psychology. They are regular parts of the model organisms and used to examine the inner workings of the brain. Perhaps the most exciting aspect of neural networks is the possibility that one day “consious networks could be established. There are a number of scientists believe that consciousness is” mechanics “of property, and that” consious “Neural networks are a realistic possibility. Finally, I want to say that although neural networks have enormous potential, we get the best of them, if integrated with information technology, artificial intelligence, fuzzy logic and related subjects.
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