Introduction To Neural Networks Using Matlab 6 0 S N Sivanandam Sumathi Deepa REPACK

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Introduction To Neural Networks Using Matlab 6 0 S N Sivanandam Sumathi Deepa

to determine the optimal values of the weights, the gradient method is used. the weights are continuously adjusted until some stopping criterion, e.g. the improvement of the overall performance, is satisfied. the stopping criteria includes the mean and variance of the euclidean distances of the weights. the error function, as defined in this paper, is defined by the euclidean distance for two sets of weights. the means and variances are adjusted iteratively, such that at each iteration the mean and variance do not exceed their previous values, and, at the same time, the error function is minimized.

numeric input neurons are different neurons in the basic network, as shown in figure 1. the function of these neurons is to recognize numeric data, such as digits, based on input patterns. neural networks also have sensory neurons, which are used to recognize sensory data, as shown in figure 2. the number of sensory neurons are equal to the input neurons. the weight is the connection between the input and hidden layer and this connectivity is learned using the back propagation or perceptron learning rule, as described in the paper.

nonlinear input neurons are of two types as shown in figure 3. both are used for generic input patterns of the form x⁰, x¹ x². the function of these neurons is to recognize generic numerical data, as shown in figure 1. therefore, both of these neurons can recognize digits such as 0,1,2,3,4,5,6,7,8,9. there are also sensory neurons for each of these neurons, as shown in figure 2.

in the basic network, there are hidden neurons and the weight is the connection between these neurons and the input layer. this weights are assigned as a learned set of values using the perceptron learning rule, as described in the paper.

artificial neural network is a method based on the biological model of human brain function. it is a computational model of the central nervous system which can learn to solve the problems. network is defined by an interconnected group of elements called neurons. neurons have been observed to be capable of complex interactions by connection to other neurons. these connections are directed and are termed synapses. each neuron receives an input from some other external source, then it converts it into an output signal and finally it generates an output to the other neurons. these connections are weighted to account for various number of incoming and outgoing connections. this setup of the neuron is called a neural network. the networks are used to solve the problems such as motion detection and defect detection. artificial neural network is a method based on the biological model of human brain function. neural network is widely used to solve the engineering problems. the basic or primitive task in the neural network is learning. learning involves changing the synaptic weights, rather than the input or output neurons in response to their input signals. fig.3. training data patterns and test data patterns introduction to neural networks using matlab 6 0 s n sivanandam sumathi deepa neural network is based on a theory which states that the body functions such as vision, hearing, speech, and sense of touch are controlled by specialized cells, neurons which are connected by synapses. the brain consists of neuron, synapses and the brain’s organ structure. these neurons are connected with their respective counterparts in the brain through synapses. the synaptic connection is a connection between the presynaptic neuron and the post synaptic neuron. the synaptic connection weights are kept as constant during learning and the synaptic connection weights are varied by the learning process. the presynaptic neuron is stimulated to send a signal to the post-synaptic neuron. the amount of signal is determined by the weight of the synapse. the synaptic connection of the cell is modified based on an error in the form of a positive or negative value. if the error is positive, the synaptic connection weight is increased and vice versa for a negative error. based on the activity and reactions to the stimuli given, either an excitatory or inhibitory signal is sent from the presynaptic neuron. this signal is passed through the synaptic connection to the postsynaptic neuron, and it is then transmitted to the next neuron. this process is repeated for each synaptic connection and neuron until the error is reduced to zero. in this way learning is accomplished. the neurons which are connected with each other are called the neurons in the neural network. based on the input to the neuron, it generates an output which is called the response and it is a product of the weights. the response will be either excitatory or inhibitory. the response is given as a signal which is sent to the next neuron.
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