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In this paper we compare the performance of back propagation and resilient propagation algorithms in training neural networks for spam classification. Back propagation algorithm is known to have issues such as slow convergence, and stagnation of neural network weights around local optima. Researchers have proposed resilient propagation as an alternative. Resilient propagation and back propagation...
The neuron machine (NM) is a hardwarearchitecture that can be used to design efficient neural networksimulation systems. However, owing to its intrinsicunidirectional nature, NM architecture does not supportbackpropagation (BP) learning algorithms. This paperproposes novel schemes for NM architecture to support BPalgorithms. Reverse-mapping memories, synapse placementalgorithm, and a memory structure...
A new scheme for adaptive neural networks for nonlinear dynamic system identification is proposed in this paper. The network of structure multi-layer perceptron with external recurrence is trained offline at first to get the initial network parameters. The parameters of the network are classified into short-term memory part and long-term memory part. The short-term memory part includes the parameters...
In this paper we train an Artificial Neural Network (ANN) using Memetic Algorithm (MA) and evaluate its performance on the UCI spambase dataset. The Memetic algorithm incorporates the local search capacity of Simulated Annealing (SA) and the global search capability of Genetic Algorithm (GA) to optimize the parameters of the ANN. The performance of the MA is compared with traditional GA in training...
This paper presents the development of anonparametric model that represents the dynamic behaviourof a flexible beam system utilizing several artificial neuralnetwork algorithms. Input-output data used in this study isobtained from Finite Difference algorithm's simulation. Thealgorithm is validated through comparison of its naturalfrequencies of vibration with the theoretical values. For systemidentification,...
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