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Artificial Neural Systems
INTRODUCTION
In machine learning and cognitive science, artificial neural networks (ANNs) is a network inspired by biological neural networks (the central nervous systems of animals, in particular the brain) which are used to estimate or approximate functions that can depend on a large number of inputs that are generally unknown.
Artificial neural networks are typically specified using three things:
Architecture specifies what variables are involved in the network and their topological relationships for example the variables involved in a neural network might be the weights of the connections between the neurons, along with activities of the neurons
Activity Rule Most neural network models have short time-scale dynamics: local rules define how the activities of the neurons change in response to each other. Typically the activity rule depends on the weights (the parameters) in the network.
Learning Rule The learning rule specifies the way in which the neural network's weights change with time. This learning is usually viewed as taking place on a longer time scale than the time scale of the dynamics under the activity rule. Usually the learning rule will depend on the activities of the neurons. It may also depend on the values of the target values supplied by a teacher and on the current value of the weights.
For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, the output neuron that determines which character was read is activated.
Like other machine learning methods systems that learn from data neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are hard to solve using ordinary rule-based programming.
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In machine learning and cognitive science, artificial neural networks (ANNs) is a network inspired by biological neural networks (the central nervous systems of animals, in particular the brain) which are used to estimate or approximate functions that can depend on a large number of inputs that are generally unknown. Artificial neural networks are typically specified using three things:
Architecture specifies what variables are involved in the network and their topological relationships for example the variables involved in a neural network might be the weights of the connections between the neurons, along with activities of the neurons
Activity Rule Most neural network models have short time-scale dynamics: local rules define how the activities of the neurons change in response to each other. Typically the activity rule depends on the weights (the parameters) in the network.
Learning Rule The learning rule specifies the way in which the neural network's weights change with time. This learning is usually viewed as taking place on a longer time scale than the time scale of the dynamics under the activity rule. Usually the learning rule will depend on the activities of the neurons. It may also depend on the values of the target values supplied by a teacher and on the current value of the weights.
For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, the output neuron that determines which character was read is activated.
Like other machine learning methods systems that learn from data neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are hard to solve using ordinary rule-based programming.
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A Basic Introduction To Neural Networks
What Is A Neural Network?
The simplest definition of a neural network, more properly referred to as an 'artificial' neural network (ANN), is provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen. He defines a neural network as:
"..a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.
In "Neural Network Primer: Part I" by Maureen Caudill, AI Expert, Feb. 1989
ANNs are processing devices (algorithms or actual hardware) that are loosely modeled after the neuronal structure of the mamalian cerebral cortex but on much smaller scales. A large ANN might have hundreds or thousands of processor units, whereas a mamalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior. Although ANN researchers are generally not concerned with whether their networks accurately resemble biological systems, some have. For example, researchers have accurately simulated the function of the retina and modeled the eye rather well.
Although the mathematics involved with neural networking is not a trivial matter, a user can rather easily gain at least an operational understanding of their structure and function.
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A basic introduction to neural networks
What is a neural network?
A neural network is a simple definition, and more properly to an "artificial neural network (ANN), ' one of the first neurocomputers Dr. Robert Hecht-Nielsen is provided by the inventor of the referred to. He said that as a neural network defines:
".. A computing system is simple, has a number of highly interconnected processing elements, external inputs for their dynamic State feedback is information about the process.
Maureen Caudill, AI Expert, February 1989: "part I neural network Primer" in
ANNs tools (algorithms or the actual hardware) that loosely modeled after neuronal structure of cerebral cortex mamalian but on much smaller scales are processed. While a mamalian magnitude of their overall brain interact and casual behavior with a corresponding increase in the billions of neurons is a large n, may be hundreds or thousands of processor units. Although ANN researchers generally with your network to accurately resemble biological systems that do not have some people worried. For example, the researchers right retina function fake and rather well eye is modelled.
Although mathematics Neural Networking is not a small thing with, easily a user at least understand their structure and can achieve an operational function.
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solution manual to introduction to artificial neural systems by zurada
