22-05-2012, 12:47 PM
advanced leaning methodologies in artificial neural networks
[attachment=22692]
INTRODUCTION
How the Brain works, the way it does, has been one of the most fascinating questions of all Times. Since the Early Times, People have been wondering regarding the extraordinary functionality of the Brain. With the Advancements made in the field of Medicine, a lot of Information regarding the functioning of the Brain has been revealed. It is a matter of Unquestionable Evidence that all the fascinating Discoveries and Inventions made by Man are all the results of this Incredible functionality of the Brain. The developments made in the field of Computer Science brought new lights into the life of man. Just as the Mechanical machines like the Spinning jenny, Steam Engine, crane, motor car helped him the Mechanical aspects doing things which he himself cannot do (a Crane could lift 1000 Kg of load where as even 10 persons struggle to do that), the Computer helped Man in mental aspects that involved Tedious calculations. Fantasies about structures that could aid man in thinking aspects like decision making, unguided implementation etc, led to the development of Artificial Intelligence. Artificial Neural networks comprise one of the key branches of Artificial Intelligence.
An Artificial Neural Network (ANN) 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 unison to solve specific problems. ANNs, like people, learn by example. An ANN 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 ANNs as well. Adjusting the Synaptic weights of the ANNs is something like changing the value of a Potentiometer setting until the desires output is reached.
Many important advances have been boosted by the use of inexpensive computer emulations. Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras. The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits. But the technology available at that time did not allow them to do too much. Currently, the neural network field enjoys a resurgence of interest and a corresponding increase in funding. Neural Networks pose promising applications in the fields like Pattern recognition, Space Applications, classifications, Predictive Algorithm Generations etc.
DISTINGUISHING FEATURES:
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. This expert can then be used to provide projections given new situations
Other advantages include:
• Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
• Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time.
• Real Time Operation: ANN 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 network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.
Neural networks do not perform miracles. But if used sensibly they can produce some amazing results.
NEURAL NETWORKS VERSUS 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.
[attachment=22692]
INTRODUCTION
How the Brain works, the way it does, has been one of the most fascinating questions of all Times. Since the Early Times, People have been wondering regarding the extraordinary functionality of the Brain. With the Advancements made in the field of Medicine, a lot of Information regarding the functioning of the Brain has been revealed. It is a matter of Unquestionable Evidence that all the fascinating Discoveries and Inventions made by Man are all the results of this Incredible functionality of the Brain. The developments made in the field of Computer Science brought new lights into the life of man. Just as the Mechanical machines like the Spinning jenny, Steam Engine, crane, motor car helped him the Mechanical aspects doing things which he himself cannot do (a Crane could lift 1000 Kg of load where as even 10 persons struggle to do that), the Computer helped Man in mental aspects that involved Tedious calculations. Fantasies about structures that could aid man in thinking aspects like decision making, unguided implementation etc, led to the development of Artificial Intelligence. Artificial Neural networks comprise one of the key branches of Artificial Intelligence.
An Artificial Neural Network (ANN) 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 unison to solve specific problems. ANNs, like people, learn by example. An ANN 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 ANNs as well. Adjusting the Synaptic weights of the ANNs is something like changing the value of a Potentiometer setting until the desires output is reached.
Many important advances have been boosted by the use of inexpensive computer emulations. Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras. The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits. But the technology available at that time did not allow them to do too much. Currently, the neural network field enjoys a resurgence of interest and a corresponding increase in funding. Neural Networks pose promising applications in the fields like Pattern recognition, Space Applications, classifications, Predictive Algorithm Generations etc.
DISTINGUISHING FEATURES:
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. This expert can then be used to provide projections given new situations
Other advantages include:
• Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
• Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time.
• Real Time Operation: ANN 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 network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.
Neural networks do not perform miracles. But if used sensibly they can produce some amazing results.
NEURAL NETWORKS VERSUS 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.