The design and analysis of effective and efficient neural networks and their applications
A complicated design issue of efficient Multilayer neural networks is addressed, and the perception and similar neural networks are examined. It shows that a three-layer perceptron neural network with specially designed learning algorithms provides an efficient framework to solve an exclusive OR problem using only n {minus} 1 processing elements in the second layer. Two efficient rapidly converging algorithms for any symmetric Boolean function were developed using only n {minus} 1 processing elements in the perceptron neural network and int(n/2) processing elements in the Adaline and perceptron neural network with the stepfunction transfer function. Similar results were obtained for the quasi-symmetric Boolean functions using a linear number of processing elements in perceptron neural networks, Adaline's, and perceptron neural networks with the stepfunction transfer functions. Generalized Boolean functions are discussed and two rapidly converging algorithms are shown for perceptron neural networks, Adaline's, and perceptron neural network with stepfunction transfer function. Many other interesting perceptron neural networks are discussed in the dissertation. Perceptron neural networks are applied to find the largest value of the n inputs. A new perceptron neural network is designed to find the largest value of the n inputs with the minimum number of inputs and the minimum number of layers. New perceptron neural networks are developed to sort n inputs. New, effective and efficient back-propagation Neural networks are designed to sort n inputs. The Sigmoid transfer function was discussed and a generalized Sigmoid function to improve Neural network performance was developed. A modified back-propagation learning algorithm was developed that builds any n input symmetric Boolean function using only int(n/2) processing elements in the second layer.
- Research Organization:
- Union Inst., Cincinnati, OH (USA)
- OSTI ID:
- 5693028
- Country of Publication:
- United States
- Language:
- English
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