IJCNN International Joint Conference on Neural Networks: July 8-12, 1991, Washington State Convention & Trade Center, Seattle, WA, Volume 1IEEE, 1991 - Neural circuitry |
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Page 255
... output layer are the sigmoid one , the number of the hidden layer is one , and the unit number is 129 in the input layer , 16 in the hidden layer and 129 in the output layer . Fault Discrimination Network The feed - forward Multi ...
... output layer are the sigmoid one , the number of the hidden layer is one , and the unit number is 129 in the input layer , 16 in the hidden layer and 129 in the output layer . Fault Discrimination Network The feed - forward Multi ...
Page 256
... output value of the corresponding unit in the output layer to the input pattern is the maximum among the output units and is greater than 0.5 . 2. The output value of the others are smaller than 0.5 . 3. The difference of the output ...
... output value of the corresponding unit in the output layer to the input pattern is the maximum among the output units and is greater than 0.5 . 2. The output value of the others are smaller than 0.5 . 3. The difference of the output ...
Page 362
... unit boy Ohidden unit the boy no N0 output unit Figure 1 : An example of a fully recurrent neural network . In the left figure , a well- formed sentence : " the man saw the boy " is given . The network must generate " 1 " at the output unit ...
... unit boy Ohidden unit the boy no N0 output unit Figure 1 : An example of a fully recurrent neural network . In the left figure , a well- formed sentence : " the man saw the boy " is given . The network must generate " 1 " at the output unit ...
Contents
A Neurocomputational Approach | 11 |
Particle Tracking by Deformable Templates | 7 |
Comparison of Perceptron Training by Linear Programming and by the Perceptron Convergence | 8 |
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Common terms and phrases
accuracy algorithm analog annealing applications approach approximation architecture artificial neural network associated keywords backpropagation binary Boltzmann machine chip circuit classifier coefficients components compression computation convergence corresponding data set defined deformable templates detection detector diagnosis digits document dynamic thesaurus error estimate feature map feedforward Figure filter gradient gradient descent hidden layer hidden units Hough transform IEEE implementation input layer input pattern input vector Kohonen learning algorithm learning rule linear matrix measure method minimize multilayer perceptron neural net neurons noise nonlinear obtained operation optimal output layer output unit Parallel Distributed Processing parameters pattern recognition perceptron performance pi-sigma network pixels problem Projection Pursuit proposed recurrent neural networks sample segment self-organizing sensor shown sigmoid function signal simulated annealing simulation solution summing units techniques test set threshold tracks training set update values valve plate variables vector quantization weight vector