Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
On the capabilities of multilayer perceptrons
Journal of Complexity - Special Issue on Neural Computation
Multilayer feedforward networks are universal approximators
Neural Networks
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
International Journal of Approximate Reasoning
Underwater target classification using wavelet packets and neural networks
IEEE Transactions on Neural Networks
Classification ability of single hidden layer feedforward neural networks
IEEE Transactions on Neural Networks
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A novel method for the classification of material type and its surface roughness by means of a lightweight plunger probe and optical mouse is presented in this paper. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The time and features of bouncing signal are related to the material type and its surface properties, and each material has a unique set of such properties. During the bouncing of the probe, a time varying signal is generated from optical mouse that is recorded in a data file on PC. Some dominant unique features are then extracted using digital signal processing tools to optimize neural network based classifier used in the existing system. The classifier is developed on the basis of application of supervised structures of neural networks. For this, an optimum Multilayer Perceptron Neural Network (MLP NN) model is designed to maximize accuracy under the constraints of minimum network dimension. Conjugate-gradient learning algorithm, which provides faster rate convergence, has been found suitable for the training of the MLP NN. The optimal parameters of MLP NN model based on various performance measures that also includes the receiver operating characteristics curve and classification accuracy on the testing data sets even after attempting different data partitions are determined. The classification accuracy of MLP NN is found reasonable consistently in respect of rigorous testing using different data partitions.